OpenRaiser/Intern-Atlas huggingface dataset:
3D Scene priors for road detection
Authors: José M. Álvarez 0004, Theo Gevers, Antonio M. López 0001
Venue: CVPR
Year: 2010

Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods.
Monocular 3D pose estimation and tracking by detection
Authors: Mykhaylo Andriluka, Stefan Roth 0001, Bernt Schiele
Venue: CVPR
Year: 2010

Automatic recovery of 3D human pose from monocular image sequences is a challenging and important research topic with numerous applications. Although current methods are able to recover 3D pose for a single person in controlled environments, they are severely challenged by real-world scenarios, such as crowded street scenes. To address this problem, we propose a three-stage process building on a number of recent advances. The first stage obtains an initial estimate of the 2D articulation and viewpoint of the person from single frames. The second stage allows early data association across frames based on tracking-by-detection. These two stages successfully accumulate the available 2D image evidence into robust estimates of 2D limb positions over short image sequences (= tracklets). The third and final stage uses those tracklet-based estimates as robust image observations to reliably recover 3D pose. We demonstrate state-of-the-art performance on the HumanEva II benchmark, and also show the applicability of our approach to articulated 3D tracking in realistic street conditions.
Automatic attribution of ancient Roman imperial coins
Authors: Ognjen Arandjelovic
Venue: CVPR
Year: 2010

Classification of coins is an important but laborious aspect of numismatics - the field that studies coins and currency. It is particularly challenging in the case of ancient coins. Due to the way they were manufactured, as well as wear from use and exposure to chemicals in the soil, the same ancient coin type can exhibit great variability in appearance. We demonstrate that geometry-free models of appearance do not perform better than chance on this task and that only a small improvement is gained by previously proposed models of combined appearance and geometry. Thus, our first major contribution is a new type of feature which is efficient in terms of computational time and storage requirements, and which effectively captures geometric configurations between descriptors corresponding to local features. Our second contribution is a description of a fully automatic system based on the proposed features, which robustly localizes, segments out and classifies coins from cluttered images. We also describe a large database of ancient coins that we collected and which will be made publicly available. Finally, we report the results of empirical comparison of different coin matching techniques. The features proposed in this paper are found to greatly outperform existing methods.
Direct image alignment of projector-camera systems with planar surfaces
Authors: Samuel Audet, Masatoshi Okutomi, Masayuki Tanaka 0001
Venue: CVPR
Year: 2010

Projector-camera systems use computer vision to analyze their surroundings and display feedback directly onto real world objects, as embodied by spatial augmented reality. To be effective, the display must remain aligned even when the target object moves, but the added illumination causes problems for traditional algorithms. Current solutions consider the displayed content as interference and largely depend on channels orthogonal to visible light. They cannot directly align projector images with real world surfaces, even though this may be the actual goal. We propose instead to model the light emitted by projectors and reflected into cameras, and to consider the displayed content as additional information useful for direct alignment. We implemented in software an algorithm that successfully executes on planar surfaces of diffuse reflectance properties at almost two frames per second with subpixel accuracy. Although slow, our work proves the viability of the concept, paving the way for future optimization and generalization.
Graph cut segmentation with a global constraint: Recovering region distribution via a bound of the Bhattacharyya measure
Authors: Ismail Ben Ayed, Hua-mei Chen, Kumaradevan Punithakumar, Ian G. Ross, Shuo Li 0001
Venue: CVPR
Year: 2010

This study investigates an efficient algorithm for image segmentation with a global constraint based on the Bhattacharyya measure. The problem consists of finding a region consistent with an image distribution learned a priori. We derive an original upper bound of the Bhattacharyya measure by introducing an auxiliary labeling. From this upper bound, we reformulate the problem as an optimization of an auxiliary function by graph cuts. Then, we demonstrate that the proposed procedure converges and give a statistical interpretation of the upper bound. The algorithm requires very few iterations to converge, and finds nearly global optima. Quantitative evaluations and comparisons with state-of-the-art methods on the Microsoft GrabCut segmentation database demonstrated that the proposed algorithm brings improvements in regard to segmentation accuracy, computational efficiency, and optimality. We further demonstrate the flexibility of the algorithm in object tracking.
Finding image distributions on active curves
Authors: Ismail Ben Ayed, Amar Mitiche, Mohamed Ben Salah, Shuo Li 0001
Venue: CVPR
Year: 2010

This study investigates an active curve functional which measures a similarity between the distribution of an image feature on the curve and a model distribution learned a priori. The curve evolution equation resulting from the minimization of this contour-based functional can be viewed as a geodesic active contour with a variable stopping function. The variable stopping function depends on the distribution of image feature on the curve and, therefore, can deal with difficult cases where the desired boundary corresponds to very weak image transitions. We ran several experiments supported by quantitative performance evaluations over several examples of segmentation and tracking of the left ventricle inner and outer boundaries in cardiac magnetic resonance image sequences. The results are significantly more accurate than with region-based and edge-based functionals.
On detection of multiple object instances using hough transforms
Authors: Olga Barinova, Victor S. Lempitsky, Pushmeet Kohli
Venue: CVPR
Year: 2010

To detect multiple objects of interest, the methods based on Hough transform use non-maxima supression or mode seeking in order to locate and to distinguish peaks in Hough images. Such postprocessing requires tuning of extra parameters and is often fragile, especially when objects of interest tend to be closely located. In the paper, we develop a new probabilistic framework that is in many ways related to Hough transform, sharing its simplicity and wide applicability. At the same time, the framework bypasses the problem of multiple peaks identification in Hough images, and permits detection of multiple objects without invoking nonmaximum suppression heuristics. As a result, the experiments demonstrate a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.
Minimum length in the tangent bundle as a model for curve completion
Authors: Guy Ben-Yosef, Ohad Ben-Shahar
Venue: CVPR
Year: 2010

The phenomenon of visual curve completion, where the visual system completes the missing part (e.g., due to occlusion) between two contour fragments, is a major problem in perceptual organization research. Previous computational approaches for the shape of the completed curve typically follow formal descriptions of desired, image-based perceptual properties (e.g, minimum total curvature, roundedness, etc.). Unfortunately, however, it is difficult to determine such desired properties psychophysically and indeed there is no consensus in the literature for what they should be. Instead, in this paper we suggest to exploit the fact that curve completion occurs in early vision in order to formalize the problem in a space that explicitly abstracts the primary visual cortex. We first argue that a suitable abstraction is the unit tangent bundle R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> × S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and then we show that a basic principle of “minimum energy consumption” in this space, namely a minimum length completion, entails desired perceptual properties for the completion in the image plane. We present formal theoretical analysis and numerical solution methods, we show results on natural images and their advantage over existing popular approaches, and we discuss how our theory explains recent findings from the perceptual literature using basic principles only.
Finding dots: Segmentation as popping out regions from boundaries
Authors: Elena Bernardis, Stella X. Yu
Venue: CVPR
Year: 2010

Many applications need to segment out all small round regions in an image. This task of finding dots can be viewed as a region segmentation problem where the dots form one region and the areas between dots form the other. We formulate it as a graph cuts problem with two types of grouping cues: short-range attraction based on feature similarity and long-range repulsion based on feature dissimilarity. The feature we use is a pixel-centric relational representation that encodes local convexity: Pixels inside the dots and outside the dots become sinks and sources of the feature vector. Normalized cuts on both attraction and repulsion pop out all the dots in a single binary segmentation. Our experiments show that our method is more accurate and robust than state-of-art segmentation algorithms on four categories of microscopic images. It can also detect textons in natural scene images with the same set of parameters.
Real-time tracking of multiple occluding objects using level sets
Authors: Charles Bibby, Ian D. Reid 0001
Venue: CVPR
Year: 2010

We derive a probabilistic framework for robust, realtime, visual tracking of multiple previously unseen objects from a moving camera. This framework models the discrete depth ordering of the objects being tracked in the scene. The method uses the observed image data to compute a posterior over the objects' poses, shapes and relative depths. The poses are group transformations, the shapes are implicit contours represented using level-sets and the relative depths give the discrete depth ordering of the objects. All nuisance variables are marginalised out at the pixel-level resulting in a pixel-wise posterior, as opposed to a pixel-wise likelihood, and we show using quantitative results that this provides increased resilience to noise. We also demonstrate how motion models can be incorporated within the same probabilistic framework and show how this enables the system to track complete occlusions. The effectiveness of our method is demonstrated on a variety of challenging video sequences.
Pose-robust albedo estimation from a single image
Authors: Soma Biswas, Rama Chellappa
Venue: CVPR
Year: 2010

We present a stochastic filtering approach to perform albedo estimation from a single non-frontal face image. Albedo estimation has far reaching applications in various computer vision tasks like illumination-insensitive matching, shape recovery, etc. We extend the formulation proposed in that assumes face in known pose and present an algorithm that can perform albedo estimation from a single image even when pose information is inaccurate. 3D pose of the input face image is obtained as a byproduct of the algorithm. The proposed approach utilizes class-specific statistics of faces to iteratively improve albedo and pose estimates. Illustrations and experimental results are provided to show the effectiveness of the approach. We highlight the usefulness of the method for the task of matching faces across variations in pose and illumination. The facial pose estimates obtained are also compared against ground truth.
Learning mid-level features for recognition
Authors: Y-Lan Boureau, Francis R. Bach, Yann LeCun, Jean Ponce
Venue: CVPR
Year: 2010

Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of intermediate complexity. This process can often be broken down into two steps: (1) a coding step, which performs a pointwise transformation of the descriptors into a representation better adapted to the task, and (2) a pooling step, which summarizes the coded features over larger neighborhoods. Several combinations of coding and pooling schemes have been proposed in the literature. The goal of this paper is threefold. We seek to establish the relative importance of each step of mid-level feature extraction through a comprehensive cross evaluation of several types of coding modules (hard and soft vector quantization, sparse coding) and pooling schemes (by taking the average, or the maximum), which obtains state-of-the-art performance or better on several recognition benchmarks. We show how to improve the best performing coding scheme by learning a supervised discriminative dictionary for sparse coding. We provide theoretical and empirical insight into the remarkable performance of max pooling. By teasing apart components shared by modern mid-level feature extractors, our approach aims to facilitate the design of better recognition architectures.
Transform coding for fast approximate nearest neighbor search in high dimensions
Authors: Jonathan Brandt
Venue: CVPR
Year: 2010

We examine the problem of large scale nearest neighbor search in high dimensional spaces and propose a new approach based on the close relationship between nearest neighbor search and that of signal representation and quantization. Our contribution is a very simple and efficient quantization technique using transform coding and product quantization. We demonstrate its effectiveness in several settings, including large-scale retrieval, nearest neighbor classification, feature matching, and similarity search based on the bag-of-words representation. Through experiments on standard data sets we show it is competitive with state-of-the-art methods, with greater speed, simplicity, and generality. The resulting compact representation can be the basis for more elaborate hierarchical search structures for sub-linear approximate search. However, we demonstrate that optimized linear search using the quantized representation is extremely fast and trivially parallelizable on modern computer architectures, with further acceleration possible by way of GPU implementation.
Pareto-optimal dictionaries for signatures
Authors: Michael Calonder, Vincent Lepetit, Pascal Fua
Venue: CVPR
Year: 2010

We present an effective method to optimize over the parameters of an image patch descriptor to obtain one that is computationally more efficient while maintaining a high recognition rate. We formulate the optimization problem in a multi-objective manner, which balances two conflicting goals while removing the need for traditional weighting coefficients. To this end we introduce the Pareto efficiency criterion, which helps finding solutions that increase one objective without decreasing the other. Despite the vast size of the search space, we show how a state-of-the-art Genetic Algorithm can be tailored to find good solutions. Not only does the resulting descriptor perform better than state-of-the-art ones, but our approach is of broader significance as optimization problems with balanced goals are often encountered in Computer Vision.
Cross-dataset action detection
Authors: Liangliang Cao, Zicheng Liu 0001, Thomas S. Huang
Venue: CVPR
Year: 2010

In recent years, many research works have been carried out to recognize human actions from video clips. To learn an effective action classifier, most of the previous approaches rely on enough training labels. When being required to recognize the action in a different dataset, these approaches have to re-train the model using new labels. However, labeling video sequences is a very tedious and time-consuming task, especially when detailed spatial locations and time durations are required. In this paper, we propose an adaptive action detection approach which reduces the requirement of training labels and is able to handle the task of cross-dataset action detection with few or no extra training labels. Our approach combines model adaptation and action detection into a Maximum a Posterior (MAP) estimation framework, which explores the spatial-temporal coherence of actions and makes good use of the prior information which can be obtained without supervision. Our approach obtains state-of-the-art results on KTH action dataset using only 50% of the training labels in tradition approaches. Furthermore, we show that our approach is effective for the cross-dataset detection which adapts the model trained on KTH to two other challenging datasets.
Spatial-bag-of-features
Authors: Yang Cao 0008, Changhu Wang, Zhiwei Li 0006, Liqing Zhang 0001, Lei Zhang 0001
Venue: CVPR
Year: 2010

In this paper, we study the problem of large scale image retrieval by developing a new class of bag-of-features to encode geometric information of objects within an image. Beyond existing orderless bag-of-features, local features of an image are first projected to different directions or points to generate a series of ordered bag-of-features, based on which different families of spatial bag-of-features are designed to capture the invariance of object translation, rotation, and scaling. Then the most representative features are selected based on a boosting-like method to generate a new bag-of-features-like vector representation of an image. The proposed retrieval framework works well in image retrieval task owing to the following three properties: 1) the encoding of geometric information of objects for capturing objects' spatial transformation, 2) the supervised feature selection and combination strategy for enhancing the discriminative power, and 3) the representation of bag-of-features for effective image matching and indexing for large scale image retrieval. Extensive experiments on 5000 Oxford building images and 1 million Panoramio images show the effectiveness and efficiency of the proposed features as well as the retrieval framework.
Face recognition with learning-based descriptor
Authors: Zhimin Cao, Qi Yin, Xiaoou Tang, Jian Sun 0001
Venue: CVPR
Year: 2010

We present a novel approach to address the representation issue and the matching issue in face recognition (verification). Firstly, our approach encodes the micro-structures of the face by a new learning-based encoding method. Unlike many previous manually designed encoding methods (e.g., LBP or SIFT), we use unsupervised learning techniques to learn an encoder from the training examples, which can automatically achieve very good tradeoff between discriminative power and invariance. Then we apply PCA to get a compact face descriptor. We find that a simple normalization mechanism after PCA can further improve the discriminative ability of the descriptor. The resulting face representation, learning-based (LE) descriptor, is compact, highly discriminative, and easy-to-extract. To handle the large pose variation in real-life scenarios, we propose a pose-adaptive matching method that uses pose-specific classifiers to deal with different pose combinations (e.g., frontal v.s. frontal, frontal v.s. left) of the matching face pair. Our approach is comparable with the state-of-the-art methods on the Labeled Face in Wild (LFW) benchmark (we achieved 84.45% recognition rate), while maintaining excellent compactness, simplicity, and generalization aability across different datasets.
The automatic design of feature spaces for local image descriptors using an ensemble of non-linear feature extractors
Authors: Gustavo Carneiro 0001
Venue: CVPR
Year: 2010

The design of feature spaces for local image descriptors is an important research subject in computer vision due to its applicability in several problems, such as visual classification and image matching. In order to be useful, these descriptors have to present a good trade off between discriminating power and robustness to typical image deformations. The feature spaces of the most useful local descriptors have been manually designed based on the goal above, but this design often limits the use of these descriptors for some specific matching and visual classification problems. Alternatively, there has been a growing interest in producing feature spaces by an automatic combination of manually designed feature spaces, or by an automatic selection of feature spaces and spatial pooling methods, or by the use of distance metric learning methods. While most of these approaches are usually applied to specific matching or classification problems, where test classes are the same as training classes, a few works aim at the general feature transform problem where the training classes are different from the test classes. The hope in the latter works is the automatic design of a universal feature space for local descriptor matching, which is the topic of our work. In this paper, we propose a new incremental method for learning automatically feature spaces for local descriptors. The method is based on an ensemble of non-linear feature extractors trained in relatively small and random classification problems with supervised distance metric learning techniques. Results on two widely used public databases show that our technique produces competitive results in the field.