We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

Adam: A Method for Stochastic Optimization

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

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

/pdf/data-clustering-a-review-4c452f5cab.pdf

Data clustering: a review

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 consider the registration of sequences of images where the observed scene is entirely non-rigid for example a camera flying over water, a panning shot of a field of sunflowers in the wind, or footage of a crowd applauding at a sports event. In these cases, it is not possible to impose the constraint that world points have similar colour in successive views, so existing registration techniques cannot be applied. Indeed the relationship between a point's colours in successive frames is essentially a random process. However by treating the sequence of images as a set of samples from a multidimensional stochastic time-series, we can learn a stochastic model (e.g. an AR model) of the random process which generated the sequence of images. With a static camera, this stochastic model can be used to extend the sequence arbitrarily in time. Driving the model with random noise results in an infinitely varying sequence of images which always looks like the short input sequence. In this way, we can create "videotextures" which can play forever without repetition. With a moving camera, the image generation process comprises two components-a stochastic component-generated by the videotexture, and a parametric component due to the camera motion.

/pdf/stochastic-rigidity-image-registration-for-nowhere-static-48sfk5yfbp.pdf

Stochastic rigidity: image registration for nowhere-static scenes

There is good evidence that simple animals such as bees use view-based strategies to return to a familiar location but humans could use a 3D reconstruction to achieve the same goal. Assuming some noise in the storage and retrieval process, these two types of strategy give rise to different patterns of predicted errors in homing. We describe an experiment that can help distinguish between these models. Participants wore a head mounted display to carry out a homing task in immersive virtual reality. They viewed three long thin vertical poles and had to remember where they were in relation to the poles before being transported (virtually) to a new location in the scene from where they had to walk back to the original location. The experiment was conducted in both a rich-cue scene (a furnished room) and a sparse scene (no background and no floor or ceiling). As one would expect, in a rich-cue environment the overall error was smaller and in this case the ability to separate the models was reduced. However, for the sparse-cue environment the view-based model outperforms the reconstruction-based model. Specifically, the likelihood of the experimental data is similar to the likelihood of samples drawn from the view-based model (but assessed under both models) while this is not true for samples drawn from the reconstruction-based model.

Comparison of view-based and reconstruction-based models of human navigational strategy.

Modern structure from motion (SfM) remains dependent on point features to recover camera positions, meaning that reconstruction is severely hampered in low-texture environments, for example scanning a plain coffee cup on an uncluttered table. We show how 3D curves can be used to refine camera position estimation in challenging low-texture scenes. In contrast to previous work, we allow the curves to be partially observed in all images, meaning that for the first time, curve-based SfM can be demonstrated in realistic scenes. The algorithm is based on bundle adjustment, so needs an initial estimate, but even a poor estimate from a few point correspondences can be substantially improved by including curves, suggesting that this method would benefit many existing systems.

/pdf/towards-pointless-structure-from-motion-3d-reconstruction-2w4m32drj3.pdf

Towards Pointless Structure from Motion: 3D Reconstruction and Camera Parameters from General 3D Curves

The recently introduced Rational Function (RF) model permits a linear solution of epipolar geometry and lens distortion from image correspondences for a very general class of lenses. In this paper we show that the model also permits a very elegant form of the plumbline constraint which allows uncalibrated correction of lens distortion from a single image containing lines known to be straight in the world. We show how this may be expressed as a factorization problem, and discuss its behaviour with noisy data. We also introduce a simple reduced parametrization of the RF model which again models a range of existing lenses. Because the RF model provides a very simple form for the distorted lines, nonlinear minimization can compute the Sampson distance to the distorted lines, allowing fast and accurate estimation of the RF model even from noisy images.

/pdf/a-plumbline-constraint-for-the-rational-function-lens-1vubqjy90e.pdf

A Plumbline Constraint for the Rational Function Lens Distortion Model

Algorithmic differentiation (AD) allows exact computation of derivatives given only an implementation of an objective function. Although many AD tools are available, a proper and efficient implemen...

A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning

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