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

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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

Event cameras are bio-inspired sensors that asynchronously report intensity changes in microsecond resolution. DAVIS can capture high dynamics of a scene and simultaneously output high temporal resolution events and low frame-rate intensity images. In this paper, we propose a single image (potentially blurred) and events based optical flow estimation approach. First, we demonstrate how events can be used to improve flow estimates. To this end, we encode the relation between flow and events effectively by presenting an event-based photometric consistency formulation. Then, we consider the special case of image blur caused by high dynamics in the visual environments and show that including the blur formation in our model further constrains flow estimation. This is in sharp contrast to existing works that ignore the blurred images while our formulation can naturally handle either blurred or sharp images to achieve accurate flow estimation. Finally, we reduce flow estimation, as well as image deblurring, to an alternative optimization problem of an objective function using the primal-dual algorithm. Experimental results on both synthetic and real data (with blurred and non-blurred images) show the superiority of our model in comparison to state-of-the-art approaches.

Single Image Optical Flow Estimation with an Event Camera

We present a new approach for single view, image-based object pose estimation in real time. Specifically, the problem of culling false positives among several pose proposal estimates is addressed in this paper. Our proposed approach targets the problem of inaccurate confidence values predicted by CNNs which is used by many current methods to choose a final object pose prediction. We present a new network called CullNet, solving this task. CullNet takes pairs of pose masks rendered from a 3D model, and cropped regions in the original image as input. This is then used to calibrate the confidence scores of the pose proposals. This new set of confidence scores is found to be significantly more reliable for accurate object pose estimation as shown by our results. Our experimental results on multiple challenging datasets (LINEMOD and Occlusion LINEMOD) clearly reflects the utility of our proposed method. Our overall pose estimation pipeline outperforms state-of-the-art object pose estimation methods on these standard object pose estimation datasets. The code is available at https://github.com/kartikgupta-at-ANU/CullNet.

/pdf/cullnet-calibrated-and-pose-aware-confidence-scores-for-3wuxvo4997.pdf

CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation

This paper proposes a new retrieval problem and conducts the initial study. This problem aims at finding the location of an item in a supermarket by means of visual retrieval. It is modelled as object-based retrieval and approached using the local invariant features. Two existing retrieval methods are investigated and their similarity measures are modified to better fit this new problem. More importantly, through the study this new retrieval problem proves itself to be a challenging task. An instant application of it is to help the customer find what they want without physically wandering around the shelves but a wide range of potential applications could be expected.

Where's the weet-bix?

We present a motion estimation algorithm for multi-camera systems consisting of more than one calibrated camera securely attached on a moving object. So, they move all together, but do not require to have overlapping views across the cameras. The geometrically optimal solution of the motion for the multi-camera systems under Linfin norm is provided in this paper using a global optimization technique which has been introduced recently in the computer vision research field. Taking advantage of an optimal estimate of the essential matrix through searching rotation space, we provide the optimal solution for translation by using linear programming and branch & bound algorithm. Synthetic and real data experiments are conducted, and they show more robust and improved performance than the previous methods.

/pdf/motion-estimation-for-multi-camera-systems-using-global-8hj0mi2kk0.pdf

Motion estimation for multi-camera systems using global optimization

State-of-the-art supervised local descriptor learning methods heavily rely on accurately labelled patches for training. However, since the process of labelling patches is laborious and inefficient, supervised training is limited by the availability and scale of training datasets. In comparison, unsupervised learning does not require burdensome data labelling; thus it is not restricted to a specific domain. Furthermore, extracting patches from training images in-volves minimal effort. Nevertheless, most of the existing unsupervised learning based methods are inherently inferior to the handcrafted local descriptors, such as the Scale-Invariant Feature Transform (SIFT). In this paper, we aim to leverage unlabelled data to learn descriptors for image patches by a deep convolutional neural network. We introduce a Relative Distance Ranking Loss (RDRL) that measures the deviation of a generated ranking order of patch similarities against a reference one. Specifically, our approach yields a patch similarity ranking based on the learned embedding of a neural network, and the ranking mechanism minimizes the proposed RDRL by mimicking a reference similarity ranking based on a competent handcrafted feature (i.e., SIFT). To our advantage, after the training process, our network is not only able to measure the patch similarity but also able to outperform SIFT by a large margin on several commonly used benchmark datasets as demonstrated in our extensive experiments.

/pdf/unsupervised-extraction-of-local-image-descriptors-via-2m1prb4csw.pdf

Richard Hartley

Papers

Single Image Optical Flow Estimation with an Event Camera

CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation

Where's the weet-bix?

Motion estimation for multi-camera systems using global optimization

Unsupervised Extraction of Local Image Descriptors via Relative Distance Ranking Loss