This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

/pdf/distinctive-image-features-from-scale-invariant-keypoints-3waurjqzke.pdf

Distinctive Image Features from Scale-Invariant Keypoints

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

/pdf/going-deeper-with-convolutions-1yobw2o2ds.pdf

Going deeper with convolutions

Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this multiple view geometry in computer vision. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.

Multiple View Geometry in Computer Vision.

We propose a flexible technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one more step from laboratory environments to real world use.

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr98-71.pdf

A flexible new technique for camera calibration

Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multiframe stereo data sets with ground truth, and are making both the code and data sets available on the Web.

/pdf/a-taxonomy-and-evaluation-of-dense-two-frame-stereo-4c20etkubp.pdf

A taxonomy and evaluation of dense two-frame stereo correspondence algorithms

We consider an isotropic gradient model for the regularization terms in a multi-label MRF lattice. The isotropic gradient is modeled by considering 3- cliques in an 8-connected lattice. Of interest here are iterative move algorithms like alpha- expansion and alpha-beta swap, which try to minimize the energy function by solving a series of binary labeling problems. Such algorithms mainly differ in their update policy for the binary move at each iteration. Here, the aim is to study the submodularity of the binary move at each stage for a general update policy. We give the necessary and sufficient condition for the submodularity of a general update policy for the two major types of labels, namely ordered labels and unordered labels.

/pdf/move-based-algorithms-for-the-optimization-of-an-isotropic-31pwxc6gik.pdf

Move-Based Algorithms for the Optimization of an Isotropic Gradient MRF Model

Attention has been proved to be an efficient mechanism to capture long-range
dependencies. However, so far it has not been deployed in invertible networks.
This is due to the fact that in order to make a network invertible, every
component within the network needs to be a bijective transformation, but a
normal attention block is not. In this paper, we propose invertible attention
that can be plugged into existing invertible models. We mathematically and
experimentally prove that the invertibility of an attention model can be
achieved by carefully constraining its Lipschitz constant. We validate the
invertibility of our invertible attention on image reconstruction task with 3
popular datasets: CIFAR-10, SVHN, and CelebA. We also show that our invertible
attention achieves similar performance in comparison with normal non-invertible
attention on dense prediction tasks. The code is available at
https://github.com/Schwartz-Zha/InvertibleAttention

Invertible Attention.

Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty. In current aleatoric uncertainty estimation frameworks, it is often neglected that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. Since the oracle model is inaccessible in most cases, we propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Further, we show a trivial solution in the dual-head based heteroscedastic aleatoric uncertainty estimation framework and introduce a new uncertainty consistency loss to avoid it. For epistemic uncertainty estimation, we argue that the internal variable in a conditional latent variable model is another source of epistemic uncertainty to model the predictive distribution and explore the limited knowledge about the hidden true model. We validate our observation on a dense prediction task, i.e., camouflaged object detection. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.

Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model

The paper gives a practical rapid algorithm for do- ing projective reconstruction of a scene consisting of a set of lines seen in three or more images with un- calibrated cameras The algorithm is evaluated on real and ideal data to determine its performance in the presence of varying de lees of noise By carefully consideration of sources oferror, it is possible to get accurate reconstruction with realistic levels of noise The algorithm can be applied to images from differ- ent cameras or the same camera For images with the same camera with unknown calibration, it is possible to do a complete Euclidean reconstruction of the im- age This extends to the case of uncalibrated cameras previous results on scene reconstruction from lines

/pdf/projective-reconstruction-from-line-correspondences-4uu1eta5uv.pdf

Projective Reconstruction from Line Correspondences

Weakly Supervised Object Localization (WSOL) methods have become increasingly popular since they only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms. Typically, a WSOL model is first trained to predict class generic objectness scores on an off-the-shelf fully supervised source dataset and then it is progressively adapted to learn the objects in the weakly supervised target dataset. In this work, we argue that learning only an objectness function is a weak form of knowledge transfer and propose to learn a classwise pairwise similarity function that directly compares two input proposals as well. The combined localization model and the estimated object annotations are jointly learned in an alternating optimization paradigm as is typically done in standard WSOL methods. In contrast to the existing work that learns pairwise similarities, our proposed approach optimizes a unified objective with convergence guarantee and it is computationally efficient for large-scale applications. Experiments on the COCO and ILSVRC 2013 detection datasets show that the performance of the localization model improves significantly with the inclusion of pairwise similarity function. For instance, in the ILSVRC dataset, the Correct Localization (CorLoc) performance improves from 72.7% to 78.2% which is a new state-of-the-art for weakly supervised object localization task.

Richard Hartley

Papers

Move-Based Algorithms for the Optimization of an Isotropic Gradient MRF Model

Invertible Attention.

Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model

Projective Reconstruction from Line Correspondences

In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization