OpenRaiser/Intern-Atlas huggingface dataset:
Fast Variable Window for Stereo Correspondence Using Integral Images
Authors: Olga Veksler
Venue: Conference CVPR 2003
Year: 2003

We develop a fast and accurate variable window approach. The two main ideas for achieving accuracy are choosing a useful range of window sizes/shapes for evaluation and developing a new window cost which is particularly suitable for comparing windows of different sizes. The speed of our approach is due to the integral image technique, which allows computation of our window cost over any rectangular window in constant time, regardless of window size. Our method ranks in the top four on the Middlebury stereo database with ground truth, and performs best out of methods which have comparable efficiency.
A multi-view approach to motion and stereo
Authors: Richard Szeliski
Venue: Conference CVPR 1999
Year: 1999

This paper presents a new approach to computing dense depth and motion estimates from multiple images. Rather than computing a single depth or motion map from such a collection, we associate motion or depth estimates with each image in the collection (or at least some subset of the images). This has the advantage that the depth or motion of regions occluded in one image will still be represented in some other image. Thus, tasks such as novel view interpolation or motion-compensated prediction can be solved with greater fidelity. Furthermore, the natural variation in appearance between different images can be captured. To formulate motion and structure recovery, we cast the problem as a global optimization over the unknown motion or depth maps, and use robust smoothness constraints to constrain the space of possible solutions. We develop and evaluate some motion and depth estimation algorithms based on this framework.
Parallel Depth Recovery by Changing Camera Parameters
Authors: Murali Subbarao
Venue: Conference ICCV 1988
Year: 1988

A new method is described for recovering the distance of objects in a scene from images formed by lenses. The recovery is based on measuring the change in the scene&apos;s image due to a known change in the three intrinsic camera parameters: (i) distance between the lens and the image detector, (ii) focal length of the lens, and (iii) diameter of the lens aperture. The method is parallel involving simple local computations. In comparison with stereo vision and structure-frommotion methods, the correspondence problem does not arise. This method for depth-map recovery may also be used for (i) obtaining focused images (i.e. images having large depth of field) from two images having finite depth of field, and (ii) rapid autofocusing of computer controlled video cameras. 1. Introduction Here we describe a new passive ranging method which in principle is fast and involves relatively weak assumptions that are generally valid. The method is basically a generalized version of the `depth-from-focu...
Image indexing with mixture hierarchies
Authors: Nuno Vasconcelos
Venue: Conference CVPR 2001
Year: 2001

We present an image indexing method based on a hierarchical description of the density of each of the image classes in a given database. The method is similar in spirit to traditional agglomerative clustering procedures but produces a complete mixture density, instead of a representative point, at each node of the indexing tree. Estimation of the density at a given node only requires knowledge of the mixture parameters of the children nodes, not the original data. The process is very flexible and efficient, therefore suited to problems involving large databases where existing groupings may have to be combined, or new groupings created, frequently. Experimental results show that the new indexing structure consistently outperforms a linear search when both efficiency and retrieval accuracy are taken into account.
Multilabel random walker image segmentation using prior models
Authors: Leo J. Grady
Venue: Conference CVPR 2005
Year: 2005

The recently introduced random walker segmentation algorithm by Grady and Funka-Lea (2004) has been shown to have desirable theoretical properties and to perform well on a wide variety of images in practice. However, this algorithm requires user-specified labels and produces a segmentation where each segment is connected to a labeled pixel. We show that incorporation of a nonparametric probability density model allows for an extended random walkers algorithm that can locate disconnected objects and does not require user-specified labels. Finally, we show that this formulation leads to a deep connection with the popular graph cuts method by Boykov et al. (2001) and Wu and Leahy (1993).
3D head tracking using motion adaptive texture-mapping
Authors: Lisa M. Brown
Venue: Conference CVPR 2001
Year: 2001

We have developed a fast robust 3D head tracking system based on rendering a texture-mapped cylinder. In order to handle the variable frame-to-frame motion changes, the system uses motion templates, which adapt to the current size of the motion increment. The relationship between measurable pixel energy and tracking error is used to design the parameters of the adaptive algorithm. To speedup processing and decouple rotational and translational motion, 2D positional information of the neckline is utilize. The confidence at each point is computed based on the amount of information used in creating the texture map and re-rendering the face. The system can also handle large out-of-plane rotations via additional templates. If the tracker fails, it can recover using an independent face detection routine. We compare the results of our approach with the extensive results of a closely related technique.
Image representations beyond histograms of gradients: The role of gestalt descriptors
Authors: Stanley M. Bileschi, Lior Wolf
Venue: Conference CVPR 2007
Year: 2007

Histograms of orientations and the statistics derived from them have proven to be effective image representations for various recognition tasks. In this work we attempt to improve the accuracy of object detection systems by including new features that explicitly capture mid-level Gestalt concepts. Four new image features are proposed, inspired by the Gestalt principles of continuity, symmetry, closure and repetition. The resulting image representations are used jointly with existing state-of-the-art features and together enable better detectors for challenging real-world data sets. As baseline features, we use Riesenhuber and Poggio's C1 features and Dalan and Triggs' histogram of oriented gradients feature. Given that both of these baseline features have already shown state of the art performance in multiple object detection benchmarks, that our new mid-level representations can further improve detection results warrants special consideration. We evaluate the performance of these detection systems on the publicly available StreetScenes and Caltech101 databases among others.
Fast matting using large kernel matting laplacian matrices
Authors: Kaiming He, Jian Sun 0001, Xiaoou Tang
Venue: Conference CVPR 2010
Year: 2010

Image matting is of great importance in both computer vision and graphics applications. Most existing state-of-the-art techniques rely on large sparse matrices such as the matting Laplacian. However, solving these linear systems is often time-consuming, which is unfavored for the user interaction. In this paper, we propose a fast method for high quality matting. We first derive an efficient algorithm to solve a large kernel matting Laplacian. A large kernel propagates information more quickly and may improve the matte quality. To further reduce running time, we also use adaptive kernel sizes by a KD-tree trimap segmentation technique. A variety of experiments show that our algorithm provides high quality results and is 5 to 20 times faster than previous methods.
Recovering thin structures via nonlocal-means regularization with application to depth from defocus
Authors: Paolo Favaro
Venue: Conference CVPR 2010
Year: 2010

We propose a novel scheme to recover depth maps containing thin structures based on nonlocal-means filtering regularization. The scheme imposes a distributed smoothness constraint by relying on the assumption that pixels with similar colors are likely to belong to the same surface, and therefore can be used jointly to obtain a robust estimate of their depth. This scheme can be used to solve shape-from-X problems and we demonstrate its use in the case of depth from defocus. We cast the problem in a variational framework and solve it by linearizing the corresponding Euler-Lagrange equations. The linearized system is then inverted by using efficient numerical methods such as successive overrelaxations or more general methods such as conjugate gradient when the system is not diagonally dominant. One of the main benefits of this formulation is that it can handle the regularization of highly fragmented surfaces, which require large neighborhood structures typically difficult to solve efficiently with graph-based methods. We compare the performance of the proposed algorithm with methods recently proposed in the literature that are analogous to neighborhood filters. Finally, experimental results are shown on synthetic and real data.
A large-scale benchmark dataset for event recognition in surveillance video
Authors: Sangmin Oh, Anthony Hoogs, A. G. Amitha Perera, Naresh P. Cuntoor, Chia-Chih Chen, Jong Taek Lee, Saurajit Mukherjee, J. K. Aggarwal, Hyungtae Lee, Larry S. Davis, Eran Swears, Xiaoyang Wang 0001, Qiang Ji, Kishore K. Reddy, Mubarak Shah, Carl Vondrick, Hamed Pirsiavash, Deva Ramanan, Jenny Yuen, Antonio Torralba 0001, Bi Song, Anesco Fong, Amit K. Roy-Chowdhury, Mita Desai
Venue: Conference CVPR 2011
Year: 2011

We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage. Previous datasets for action recognition are unrealistic for real-world surveillance because they consist of short clips showing one action by one individual [15, 8]. Datasets have been developed for movies [11] and sports [12], but, these actions and scene conditions do not apply effectively to surveillance videos. Our dataset consists of many outdoor scenes with actions occurring naturally by non-actors in continuously captured videos of the real world. The dataset includes large numbers of instances for 23 event types distributed throughout 29 hours of video. This data is accompanied by detailed annotations which include both moving object tracks and event examples, which will provide solid basis for large-scale evaluation. Additionally, we propose different types of evaluation modes for visual recognition tasks and evaluation metrics along with our preliminary experimental results. We believe that this dataset will stimulate diverse aspects of computer vision research and help us to advance the CVER tasks in the years ahead.
Highly accurate boundary detection and grouping
Authors: Iasonas Kokkinos
Venue: Conference CVPR 2010
Year: 2010

In this work we address boundary detection and boundary grouping. We first pursue a learning-based approach to boundary detection. For this (i) we leverage appearance and context information by extracting descriptors around edgels and use them as features for classification, (ii) we use discriminative dimensionality reduction for efficiency and (iii) we use outlier-resilient boosting to deal with noise in the training set. We then introduce fractional-linear programming to optimize a grouping criterion that is expressed as a cost ratio. Our contributions are systematically evaluated on the Berkeley benchmark.
Model-based curve evolution technique for image segmentation
Authors: Andy Tsai, Anthony J. Yezzi, William M. Wells III, Clare M. Tempany, Dewey Tucker, Ayres C. Fan, W. Eric L. Grimson, Alan S. Willsky
Venue: Conference CVPR 2001
Year: 2001

We propose a model-based curve evolution technique for segmentation of images containing known object types. In particular, motivated by the work of Leventon et al. (2000), we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data, The parameters of this representation are then calculated to minimize an objective function for segmentation. We found the resulting algorithm to be computationally efficient, able to handle multidimensional data, robust to noise and initial contour placements, while at the same time, avoiding the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications.
Tour the world: building a web-scale landmark recognition engine
Authors: Yantao Zheng, Ming Zhao 0003, Yang Song 0009, Hartwig Adam, Ulrich Buddemeier, Alessandro Bissacco, Fernando Brucher, Tat-Seng Chua, Hartmut Neven
Venue: Conference CVPR 2009
Year: 2009

Modeling and recognizing landmarks at world-scale is a useful yet challenging task. There exists no readily available list of worldwide landmarks. Obtaining reliable visual models for each landmark can also pose problems, and efficiency is another challenge for such a large scale system. This paper leverages the vast amount of multimedia data on the Web, the availability of an Internet image search engine, and advances in object recognition and clustering techniques, to address these issues. First, a comprehensive list of landmarks is mined from two sources: (1) ~20 million GPS-tagged photos and (2) online tour guide Web pages. Candidate images for each landmark are then obtained from photo sharing Websites or by querying an image search engine. Second, landmark visual models are built by pruning candidate images using efficient image matching and unsupervised clustering techniques. Finally, the landmarks and their visual models are validated by checking authorship of their member images. The resulting landmark recognition engine incorporates 5312 landmarks from 1259 cities in 144 countries. The experiments demonstrate that the engine can deliver satisfactory recognition performance with high efficiency.
Maximally stable colour regions for recognition and matching
Authors: Per-Erik Forssén
Venue: Conference CVPR 2007
Year: 2007

This paper introduces a novel colour-based affine co-variant region detector. Our algorithm is an extension of the maximally stable extremal region (MSER) to colour. The extension to colour is done by looking at successive time-steps of an agglomerative clustering of image pixels. The selection of time-steps is stabilised against intensity scalings and image blur by modelling the distribution of edge magnitudes. The algorithm contains a novel edge significance measure based on a Poisson image noise model, which we show performs better than the commonly used Euclidean distance. We compare our algorithm to the original MSER detector and a competing colour-based blob feature detector, and show through a repeatability test that our detector performs better. We also extend the state of the art in feature repeatability tests, by using scenes consisting of two planes where one is piecewise transparent. This new test is able to evaluate how stable a feature is against changing backgrounds.
CRAM: Compact representation of actions in movies
Authors: Mikel D. Rodriguez
Venue: Conference CVPR 2010
Year: 2010

Thousands of hours of video are recorded every second across the world. Due to the fact that searching for a particular event of interest within hours of video is time consuming, most captured videos are never examined, and are only used in a post-factum manner. In this work, we introduce activity-specific video summaries, which provide an effective means of browsing and indexing video based on a set of events of interest. Our method automatically generates a compact video representation of a long sequence, which features only activities of interest while preserving the general dynamics of the original video. Given a long input video sequence, we compute optical flow and represent the corresponding vector field in the Clifford Fourier domain. Dynamic regions within the flow field are identified within the phase spectrum volume of the flow field. We then compute the likelihood that certain activities of relevance occur within the the video by correlating it with spatio-temporal maximum average correlation height filters. Finally, the input sequence is condensed via a temporal shift optimization, resulting in a short video clip which simultaneously displays multiple instances of each relevant activity.
Evaluation of low-level features and their combinations for complex event detection in open source videos
Authors: Amir Tamrakar, Saad Ali, Qian Yu, Jingen Liu, Omar Javed, Ajay Divakaran, Hui Cheng, Harpreet S. Sawhney
Venue: Conference CVPR 2012
Year: 2012

Low-level appearance as well as spatio-temporal features, appropriately quantized and aggregated into Bag-of-Words (BoW) descriptors, have been shown to be effective in many detection and recognition tasks. However, their effcacy for complex event recognition in unconstrained videos have not been systematically evaluated. In this paper, we use the NIST TRECVID Multimedia Event Detection (MED11 [1]) open source dataset, containing annotated data for 15 high-level events, as the standardized test bed for evaluating the low-level features. This dataset contains a large number of user-generated video clips. We consider 7 different low-level features, both static and dynamic, using BoW descriptors within an SVM approach for event detection. We present performance results on the 15 MED11 events for each of the features as well as their combinations using a number of early and late fusion strategies and discuss their strengths and limitations.
Cross domain distribution adaptation via kernel mapping
Authors: Erheng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiangtao Ren, Deepak S. Turaga, Olivier Verscheure
Venue: Conference KDD 2009
Year: 2009

When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the assumption made by existing approaches, that the marginal and conditional probabilities are directly related between source and target domains, has limited applicability in either the original space or its linear transformations. To solve this problem, we propose an adaptive kernel approach that maps the marginal distribution of targetdomain and source-domain data into a common kernel space, and utilize a sample selection strategy to draw conditional probabilities between the two domains closer. We formally show that under the kernel-mapping space, the difference in distributions between the two domains is bounded; and the prediction error of the proposed approach can also be bounded. Experimental results demonstrate that the proposed method outperforms both traditional inductive classifiers and the state-of-the-art boosting-based transfer algorithms on most domains, including text categorization and web page ratings. In particular, it can achieve around 10 % higher accuracy than other approaches for the text categorization problem. The source code and datasets are available from the authors.
Scene text extraction and translation for handheld devices
Authors: Ismail Haritaoglu
Venue: Conference CVPR 2001
Year: 2001

We describe a scene text extraction system for handheld devices to provide enhanced information perception services to the user. It uses a color camera attached to a personal digital assistant as an input device to capture scene images from the real world and it employs image enhancement and segmentation methods to extract written information from the scene, convert them to text information and show them to the user so that he/she can see both the real world and information together. We implemented a prototype application: an automatic sign/text language translation for foreign travelers, where people can use the system whenever they want to see text or signs in their own language where they are originally written in a foreign language in the scene.
Extraction of illusory linear clues in perspectively skewed documents
Authors: Maurizio Pilu
Venue: Conference CVPR 2001
Year: 2001

The article deals with the recovery of illusory linear clues from perspectively skewed documents, with the purpose of using them for rectification. The computational approach proposed implements the perceptual organization principles implicitly used in textual layouts. The numerous examples provided show that the method is robust and viewpoint and scale invariant.
Comparing belief propagation and graph cuts for novelty detection
Authors: Shyjan Mahamud
Venue: Conference CVPR 2006
Year: 2006

Novelty detection or background subtraction methods for surveillance with a fixed camera typically model each pixel independently of its neighbours. More recently [11], a Markov Random Field (MRF) prior has been used to model consistencies among neighbouring foreground/background labels. Graph Cut methods have been used to find the maximum of the resulting posterior distribution for the labels for each frame. However, for increased efficiency and accuracy, we propose the use of loopy belief propagation. A major reason for increased efficiency is the fact that the output labels from the previous frame can be used as initialisation for the current frame in belief propagation. The Graph Cuts approach is empirically compared with both the "sumproduct" as well as "max-product" rules of belief propagation on real video sequences. Significantly, while the "max-product" rule has similar peak precision-recall performance as graph-cuts, the "sum-product" rule gives even better peak performance. This can be attributed to the fact that latter rule finds the marginals over the entire posterior distribution for the labels rather than just the maximum of the posterior which is more prone to noise.
Deep complex networks
Authors: Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang 0033, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher J. Pal
Venue: Conference ICLR 2018
Year: 2018

At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and representations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks and convolutional LSTMs. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their real-valued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech Spectrum Prediction using the TIMIT dataset. We achieve state-of-the-art performance on these audio-related tasks.