OpenRaiser/Intern-Atlas huggingface dataset:
Recognizing human action in time-sequential images using hidden markov model
Authors: Junji Yamato, Jun Ohya, Kenichiro Ishii
Venue: Conference CVPR 1992
Year: 1992

A human action recognition method based on a hidden Markov model (HMM) is proposed. It is a feature-based bottom-up approach that is characterized by its learning capability and time-scale invariability. To apply HMMs, one set of time-sequential images is transformed into an image feature vector sequence, and the sequence is converted into a symbol sequence by vector quantization. In learning human action categories, the parameters of the HMMs, one per category, are optimized so as to best describe the training sequences from the category. To recognize an observed sequence, the HMM which best matches the sequence is chosen. Experimental results for real time-sequential images of sports scenes show recognition rates higher than 90%. The recognition rate is improved by increasing the number of people used to generate the training data, indicating the possibility of establishing a person-independent action recognizer.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Multiple-Hand-Gesture Tracking using Multiple Cameras
Authors: Akira Utsumi, Jun Ohya
Venue: Conference CVPR 1999
Year: 1999

We propose a method of tracking 3D position, posture, and shapes of human hands from multiple-viewpoint images. Self-occlusion and hand-hand occlusion are serious problems in the vision-based hand tracking. Our system employs multiple-viewpoint and viewpoint selection mechanism to reduce these problems. Each hand position is tracked with a Kalman filler and the motion vectors are updated with image features in selected images that do not include hand-hand occlusion. 3D hand postures are estimated with a small number of reliable image features. These features are extracted based on distance transformation, and they are robust against changes in hand shape and self-occlusion. Finally, a "best view" image is selected for each hand for shape recognition. The shape recognition process is based on a Fourier descriptor. Our system can be used as a user interface device an a virtual environment, replacing glove-type devices and overcoming most of the disadvantages of contact-type devices.
Real-Time SLAM Relocalisation
Authors: Brian Patrick Williams, Georg Klein, Ian D. Reid 0001
Venue: Conference ICCV 2007
Year: 2007

Monocular SLAM has the potential to turn inexpensive cameras into powerful pose sensors for applications such as robotics and augmented reality. However, current implementations lack the robustness required to be useful outside laboratory conditions: blur, sudden motion and occlusion all cause tracking to fail and corrupt the map. Here we present a system which automatically detects and recovers from tracking failure while preserving map integrity. By extending recent advances in keypoint recognition the system can quickly resume tracking - i.e. within a single frame time of 33 ms - using any of the features previously stored in the map. Extensive tests show that the system can reliably generate maps for long sequences even in the presence of frequent tracking failure.
Home 3d body scans from noisy image and range data
Authors: Alexander Weiss, David A. Hirshberg, Michael J. Black
Venue: Conference ICCV 2011
Year: 2011

The 3D shape of the human body is useful for applications in fitness, games and apparel. Accurate body scanners, however, are expensive, limiting the availability of 3D body models. We present a method for human shape reconstruction from noisy monocular image and range data using a single inexpensive commodity sensor. The approach combines low-resolution image silhouettes with coarse range data to estimate a parametric model of the body. Accurate 3D shape estimates are obtained by combining multiple monocular views of a person moving in front of the sensor. To cope with varying body pose, we use a SCAPE body model which factors 3D body shape and pose variations. This enables the estimation of a single consistent shape while allowing pose to vary. Additionally, we describe a novel method to minimize the distance between the projected 3D body contour and the image silhouette that uses analytic derivatives of the objective function. We propose a simple method to estimate standard body measurements from the recovered SCAPE model and show that the accuracy of our method is competitive with commercial body scanning systems costing orders of magnitude more.
Fast Directional Chamfer Matching
Authors: Ming-Yu Liu 0001, Oncel Tuzel, Ashok Veeraraghavan, Rama Chellappa
Venue: Conference CVPR 2010
Year: 2010

We study the object localization problem in images given a single hand-drawn example or a gallery of shapes as the object model. Although many shape matching algorithms have been proposed for the problem over the decades, chamfer matching remains to be the preferred method when speed and robustness are considered. In this paper, we significantly improve the accuracy of chamfer matching while reducing the computational time from linear to sublinear (shown empirically). Specifically, we incorporate edge orientation information in the matching algorithm such that the resulting cost function is piecewise smooth and the cost variation is tightly bounded. Moreover, we present a sublinear time algorithm for exact computation of the directional chamfer matching score using techniques from 3D distance transforms and directional integral images. In addition, the smooth cost function allows to bound the cost distribution of large neighborhoods and skip the bad hypotheses within. Experiments show that the proposed approach improves the speed of the original chamfer matching upto an order of 45×, and it is much faster than many state of art techniques while the accuracy is comparable.
Learning a category independent object detection cascade
Authors: Esa Rahtu, Juho Kannala, Matthew B. Blaschko
Venue: Conference ICCV 2011
Year: 2011

Cascades are a popular framework to speed up object detection systems. Here we focus on the first layers of a category independent object detection cascade in which we sample a large number of windows from an objectness prior, and then discriminatively learn to filter these candidate windows by an order of magnitude. We make a number of contributions to cascade design that substantially improve over the state of the art: (i) our novel objectness prior gives much higher recall than competing methods, (ii) we propose objectness features that give high performance with very low computational cost, and (iii) we make use of a structured output ranking approach to learn highly effective, but inexpensive linear feature combinations by directly optimizing cascade performance. Thorough evaluation on the PASCAL VOC data set shows consistent improvement over the current state of the art, and over alternative discriminative learning strategies.
Recognition using regions
Authors: Chunhui Gu, Joseph J. Lim, Pablo Arbeláez, Jitendra Malik
Venue: Conference CVPR 2009
Year: 2009

This paper presents a unified framework for object detection, segmentation, and classification using regions. Region features are appealing in this context because: (1) they encode shape and scale information of objects naturally; (2) they are only mildly affected by background clutter. Regions have not been popular as features due to their sensitivity to segmentation errors. In this paper, we start by producing a robust bag of overlaid regions for each image using Arbeldez et al., CVPR 2009. Each region is represented by a rich set of image cues (shape, color and texture). We then learn region weights using a max-margin framework. In detection and segmentation, we apply a generalized Hough voting scheme to generate hypotheses of object locations, scales and support, followed by a verification classifier and a constrained segmenter on each hypothesis. The proposed approach significantly outperforms the state of the art on the ETHZ shape database(87.1% average detection rate compared to Ferrari et al. 's 67.2%), and achieves competitive performance on the Caltech 101 database.
Class segmentation and object localization with superpixel neighborhoods
Authors: Brian Fulkerson, Andrea Vedaldi, Stefano Soatto
Venue: Conference ICCV 2009
Year: 2009

We propose a method to identify and localize object classes in images. Instead of operating at the pixel level, we advocate the use of superpixels as the basic unit of a class segmentation or pixel localization scheme. To this end, we construct a classifier on the histogram of local features found in each superpixel. We regularize this classifier by aggregating histograms in the neighborhood of each superpixel and then refine our results further by using the classifier in a conditional random field operating on the superpixel graph. Our proposed method exceeds the previously published state-of-the-art on two challenging datasets: Graz-02 and the PASCAL VOC 2007 Segmentation Challenge.
Rgb-(d) scene labeling: Features and algorithms
Authors: Xiaofeng Ren, Liefeng Bo, Dieter Fox
Venue: Conference CVPR 2012
Year: 2012

Scene labeling research has mostly focused on outdoor scenes, leaving the harder case of indoor scenes poorly understood. Microsoft Kinect dramatically changed the landscape, showing great potentials for RGB-D perception (color+depth). Our main objective is to empirically understand the promises and challenges of scene labeling with RGB-D. We use the NYU Depth Dataset as collected and analyzed by Silberman and Fergus [30]. For RGB-D features, we adapt the framework of kernel descriptors that converts local similarities (kernels) to patch descriptors. For contextual modeling, we combine two lines of approaches, one using a superpixel MRF, and the other using a segmentation tree. We find that (1) kernel descriptors are very effective in capturing appearance (RGB) and shape (D) similarities; (2) both superpixel MRF and segmentation tree are useful in modeling context; and (3) the key to labeling accuracy is the ability to efficiently train and test with large-scale data. We improve labeling accuracy on the NYU Dataset from 56.6% to 76.1%. We also apply our approach to image-only scene labeling and improve the accuracy on the Stanford Background Dataset from 79.4% to 82.9%.
Zero-shot visual imitation
Authors: Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal 0001, Dian Chen 0001, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell
Venue: Conference ICLR 2018
Year: 2018

The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy with a novel forward consistency loss. In our framework, the role of the expert is only to communicate the goals (i.e., what to imitate) during inference. The learned policy is then employed to mimic the expert (i.e., how to imitate) after seeing just a sequence of images demonstrating the desired task. Our method is 'zero-shot' in the sense that the agent never has access to expert actions during training or for the task demonstration at inference. We evaluate our zero-shot imitator in two real-world settings: complex rope manipulation with a Baxter robot and navigation in previously unseen office environments with a TurtleBot. Through further experiments in VizDoom simulation, we provide evidence that better mechanisms for exploration lead to learning a more capable policy which in turn improves end task performance. Videos, models, and more details are available at https://pathak22.github.io/zeroshot-imitation/
Reconstruction from image sequences by means of relative depths
Authors: Anders Heyden
Venue: Conference ICCV 1995
Year: 1995

The paper deals with the problem of reconstructing the locations of n points in space from m different images without camera calibration. It shows how these problems can be put into a similar theoretical framework. A new concept, the reduced fundamental matrix, is introduced. It contains just 4 parameters and can be used to predict locations of points in the images and to make reconstruction. We also introduce the concept of reduced fundamental tensor which describes the relations between points in 3 images. It has 15 components and depends on 9 parameters. Necessary and sufficient conditions for a tensor to be a reduced fundamental tensor are derived. This framework can be generalised to a sequence of images. The dependencies between the different representations are investigated. Furthermore a canonical form of the camera matrices in a sequence are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
A linear method for reconstruction from lines and points
Authors: Richard I. Hartley
Venue: Conference ICCV 1995
Year: 1995

Discusses the basic role of the trifocal tensor in scene reconstruction. This 3/spl times/3/spl times/3 tensor plays a role in the analysis of scenes from three views analogous to the role played by the fundamental matrix in the two-view case. In particular, the trifocal tensor maybe computed by a linear algorithm from a set of 13 line correspondences in three views. It is further shown in this paper to be essentially identical to a set of coefficients introduced by Shashua (1994) to effect point transfer in the three-view case. This observation means that the 13-line algorithm may be extended to allow for the computation of the trifocal tensor given any mixture of sufficiently many line and point correspondences. From the trifocal tensor, the camera image matrices may be computed, and the scene may be reconstructed. For unrelated uncalibrated cameras, this reconstruction is unique up to projectivity. Thus, projective reconstruction of a set of lines and points may be reconstructed linearly from three views.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
A minimal parameterization of the trifocal tensor
Authors: Klas Nordberg
Venue: Conference CVPR 2009
Year: 2009

The paper describes a minimal set of 18 parameters that can represent any trifocal tensor consistent with the internal constraints. 9 parameters describe three orthogonal matrices and 9 parameters describe 10 elements of a sparse tensor T̃ with 17 elements in well-defined positions equal to zero. Any valid trifocal tensor is then given as some specific T̃ transformed by the orthogonal matrices in the respective image domain. The paper also describes a simple approach for estimating the three orthogonal matrices in the case of a general 3 × 3 × 3 tensor, i.e., when the internal constraints are not satisfied. This can be used to accomplish a least squares approximation of a general tensor to a tensor that satisfies the internal constraints. This type of constraint enforcement, in turn, can be used to obtain an improved estimate of the trifocal tensor based on the normalized linear algorithm, with the constraint enforcement as a final step. This makes the algorithm more similar to the corresponding algorithm for estimation of the fundamental matrix. An experiment on synthetic data shows that the constraint enforcement of the trifocal tensor produces a significantly better result than without enforcement, expressed by the positions of the epipoles, given that the constraint enforcement is made in normalized image coordinates.
Pedestrian detection at 100 frames per second
Authors: Rodrigo Benenson, Markus Mathias, Radu Timofte, Luc Van Gool
Venue: Conference CVPR 2012
Year: 2012

We present a new pedestrian detector that improves both in speed and quality over state-of-the-art. By efficiently handling different scales and transferring computation from test time to training time, detection speed is improved. When processing monocular images, our system provides high quality detections at 50 fps. We also propose a new method for exploiting geometric context extracted from stereo images. On a single CPU+GPU desktop machine, we reach 135 fps, when processing street scenes, from rectified input to detections output.
Classification and evaluation of cost aggregation methods for stereo correspondence
Authors: Federico Tombari, Stefano Mattoccia, Luigi Di Stefano, Elisa Addimanda
Venue: Conference CVPR 2008
Year: 2008

In the last decades several cost aggregation methods aimed at improving the robustness of stereo correspondence within local and global algorithms have been proposed. Given the recent developments and the lack of an appropriate comparison, in this paper we survey, classify and compare experimentally on a standard data set the main cost aggregation approaches proposed in literature. The experimental evaluation addresses both accuracy and computational requirements, so as to outline the best performing methods under these two criteria.
Assessing data mining results via swap randomization
Authors: Aristides Gionis, Heikki Mannila, Taneli Mielikäinen, Panayiotis Tsaparas
Venue: Conference KDD 2006
Year: 2006

The problem of assessing the significance of data mining results on high-dimensional 0-1 data sets has been studied extensively in the literature. For problems such as mining frequent sets and finding correlations, significance testing can be done by, e.g., chi-square tests, or many other methods. However, the results of such tests depend only on the specific attributes and not on the dataset as a whole. Moreover, the tests are more difficult to apply to sets of patterns or other complex results of data mining. In this paper, we consider a simple randomization technique that deals with this shortcoming. The approach consists of producing random datasets that have the same row and column margins with the given dataset, computing the results of interest on the randomized instances, and comparing them against the results on the actual data. This randomization technique can be used to assess the results of many different types of data mining algorithms, such as frequent sets, clustering, and rankings. To generate random datasets with given margins, we use variations of a Markov chain approach, which is based on a simple swap operation. We give theoretical results on the efficiency of different randomization methods, and apply the swap randomization method to several well-known datasets. Our results indicate that for some datasets the structure discovered by the data mining algorithms is a random artifact, while for other datasets the discovered structure conveys meaningful information.
Spin: mining maximal frequent subgraphs from graph databases
Authors: Jun Huan, Wei Wang, Jan F. Prins, Jiong Yang
Venue: Conference KDD 2004
Year: 2004

One fundamental challenge for mining recurring subgraphs from semi-structured data sets is the overwhelming abundance of such patterns. In large graph databases, the total number of frequent subgraphs can become too large to allow a full enumeration using reasonable computational resources. In this paper, we propose a new algorithm that mines only maximal frequent subgraphs, i.e. subgraphs that are not a part of any other frequent subgraphs. This may exponentially decrease the size of the output set in the best case; in our experiments on practical data sets, mining maximal frequent subgraphs reduces the total number of mined patterns by two to three orders of magnitude.Our method first mines all frequent trees from a general graph database and then reconstructs all maximal subgraphs from the mined trees. Using two chemical structure benchmarks and a set of synthetic graph data sets, we demonstrate that, in addition to decreasing the output size, our algorithm can achieve a five-fold speed up over the current state-of-the-art subgraph mining algorithms.
Closegraph: mining closed frequent graph patterns
Authors: Xifeng Yan, Jiawei Han
Venue: Conference KDD 2003
Year: 2003

Recent research on pattern discovery has progressed form mining frequent itemsets and sequences to mining structured patterns including trees, lattices, and graphs. As a general data structure, graph can model complicated relations among data with wide applications in bioinformatics, Web exploration, and etc. However, mining large graph patterns in challenging due to the presence of an exponential number of frequent subgraphs. Instead of mining all the subgraphs, we propose to mine closed frequent graph patterns. A graph g is closed in a database if there exists no proper supergraph of g that has the same support as g. A closed graph pattern mining algorithm, CloseGraph, is developed by exploring several interesting pruning methods. Our performance study shows that CloseGraph not only dramatically reduces unnecessary subgraphs to be generated but also substantially increases the efficiency of mining, especially in the presence of large graph patterns.
Dynamic Personalized Pagerank in Entity-Relation Graphs
Authors: Soumen Chakrabarti
Venue: Conference WWW 2007
Year: 2007

Extractors and taggers turn unstructured text into entity-relation(ER) graphs where nodes are entities (email, paper, person,conference, company) and edges are relations (wrote, cited,works-for). Typed proximity search of the form <B>type=personNEAR company~"IBM", paper~"XML"</B> is an increasingly usefulsearch paradigm in ER graphs. Proximity search implementations either perform a Pagerank-like computation at query time, which is slow, or precompute, store and combine per-word Pageranks, which can be very expensive in terms of preprocessing time and space. We present HubRank, a new system for fast, dynamic, space-efficient proximity searches in ER graphs. During preprocessing, HubRank computesand indexes certain "sketchy" random walk fingerprints for a small fraction of nodes, carefully chosen using query log statistics. At query time, a small "active" subgraph is identified, bordered bynodes with indexed fingerprints. These fingerprints are adaptively loaded to various resolutions to form approximate personalized Pagerank vectors (PPVs). PPVs at remaining active nodes are now computed iteratively. We report on experiments with CiteSeer's ER graph and millions of real Cite Seer queries. Some representative numbers follow. On our testbed, HubRank preprocesses and indexes 52 times faster than whole-vocabulary PPV computation. A text index occupies 56 MB. Whole-vocabulary PPVs would consume 102GB. If PPVs are truncated to 56 MB, precision compared to true Pagerank drops to 0.55; incontrast, HubRank has precision 0.91 at 63MB. HubRank's average querytime is 200-300 milliseconds; query-time Pagerank computation takes 11 seconds on average.
Anchor-based proximity measures
Authors: Amruta Joshi, Ravi Kumar, Benjamin C. Reed, Andrew Tomkins
Venue: Conference WWW 2007
Year: 2007

We present a family of measures of proximity of an arbitrary node in a directed graph to a pre-specified subset of nodes, called the anchor. Our measures are based on three different propagation schemesand two different uses of the connectivity structure of the graph. We consider a web-specific application of the above measures with two disjoint anchors - good and bad web pages - and study the accuracy of these measures in this context.
Query suggestion using hitting time
Authors: Qiaozhu Mei, Dengyong Zhou, Kenneth Ward Church
Venue: Conference CIKM 2008
Year: 2008

Generating alternative queries, also known as query suggestion, has long been proved useful to help a user explore and express his information need. In many scenarios, such suggestions can be generated from a large scale graph of queries and other accessory information, such as the clickthrough. However, how to generate suggestions while ensuring their semantic consistency with the original query remains a challenging problem.