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Modern Applied Statistics With S

Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.

pdf/pointnet-deep-hierarchical-feature-learning-on-point-sets-in-529t7agii6.pdf

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.

/pdf/dynamic-graph-cnn-for-learning-on-point-clouds-20wlcru9tg.pdf

Dynamic Graph CNN for Learning on Point Clouds

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call "sparse rescaling". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.

/pdf/accelerated-profile-hmm-searches-5f0frwnmca.pdf

Accelerated Profile HMM Searches

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

Computer Vision: Algorithms and Applications

We introduce exotypes, user-defined types that combine the flexibility of meta-object protocols in dynamically-typed languages with the performance control of low-level languages. Like objects in dynamic languages, exotypes are defined programmatically at run-time, allowing behavior based on external data such as a database schema. To achieve high performance, we use staged programming to define the behavior of an exotype during a runtime compilation step and implement exotypes in Terra, a low-level staged programming language. We show how exotype constructors compose, and use exotypes to implement high-performance libraries for serialization, dynamic assembly, automatic differentiation, and probabilistic programming. Each exotype achieves expressiveness similar to libraries written in dynamically-typed languages but implements optimizations that exceed the performance of existing libraries written in low-level statically-typed languages. Though each implementation is significantly shorter, our serialization library is 11 times faster than Kryo, and our dynamic assembler is 3--20 times faster than Google's Chrome assembler.

/pdf/first-class-runtime-generation-of-high-performance-types-3c7qrszeuk.pdf

First-class runtime generation of high-performance types using exotypes

In the purely object-parallel approach to multiprocessor rendering, each processor is assigned responsibility to render a subset of the graphics database. When rendering is complete, pixels from the processors must be merged and globally z-buffered. On an arbitrary multiprocessor interconnection network, the straightforward algorithm for pixel merging requires d/sup -/A total network bandwidth per frame, where d/sup -/ is the depth complexity of the scene and A is the area of the screen or window. This algorithm is used by the Kubota Pacific Denali and appears to be used by the Evans and Sutherland Freedom series. An alternative algorithm, the PixelFlow algorithm, requires nA network bandwidth per frame, where n is the number of processors. But the merging is pipelined in PixelFlow so that each network link must only support A bandwidth per frame. However, that algorithm requires a separate special-purpose network for pixel merging. In this paper we present and analyze an expected-case log (d/sup -/)A algorithm for pixel merging that uses network broadcast, and we discuss the algorithm's applicability to shared-memory bus architectures.

Pixel merging for object-parallel rendering: a distributed snooping algorithm

Universal probabilistic programming languages (such as Church [6]) trade performance for abstraction: any model can be represented compactly as an arbitrary stochastic computation, but costly online analyses are required for inference. We present a technique that recovers hand-coded levels of performance from a universal probabilistic language, for the Metropolis-Hastings (MH) MCMC inference algorithm. It takes a Church program as input and traces its execution to remove computation overhead. It then analyzes the trace for each proposal, using slicing, to identify the minimal computation needed to evaluate the MH acceptance probability. Generated incremental code is much faster than a baseline implementation (up to 600x) and usually as fast as handcoded MH kernels.

/pdf/generating-efficient-mcmc-kernels-from-probabilistic-ak2xfngb7i.pdf

Generating Efficient MCMC Kernels from Probabilistic Programs

In this paper we describe a non-parametric probabilistic model that can be used to encode relationships in color naming datasets. This model can be used with datasets with any number of color terms and expressions, as well as terms from multiple languages. Because the model is based on probability theory, we can use classic statistics to compute features of interest to color scientists. In particular, we show that the uniqueness of a color name (color saliency) can be captured using the entropy of the probability distribution. We demonstrate this approach by applying this model to two different datasets: the multi-lingual World Color Survey (WCS), and a database collected via the web by Dolores Labs. We demonstrate how saliency clusters similarly named colors for both datasets, and compare our WCS results to those of Kay and his colleagues. We compare the two datasets to each other by converting them to a common colorspace (IPT). Introduction There has been growing interest in how to use color naming data to improve color models. Better color name databases[7, 10, 11, 12, 14, 2] and online naming studies[18, 8] have stimulated recent work. Color naming databases and associated models have been been useful in color transfer[5], gamut mapping[19, 20], and methods for specifying or selecting colors in an image[15, 16, 17]. In this paper, we examine the issue of how to represent and quantify the association between colors induced by names. Current methods that incorporate naming data represent the category associated with a color using either a single name[5, 6], a vector[19], or by a set of fuzzy logic memberships[1, 2, 17]. We present a probabilistic framework for working with colors. We define the categorical association of a color c as a conditional probability P(C|c) over colors C in the color space C . For a color c, the probability P(C|c) represents how likely other colors in the space C are assigned the same linguistic label as c. Our choice of using a probability over colors in our framework is motivated by the following criteria not met by current approaches. Our model satisfies three design goals. (1) Our approach can incorporate categorical effects from any number of color words, expressions involving multiple words, and different languages. (2) Our framework is based on a non-parametric model which can capture the differences in color name distributions such as “yellow” having a narrow focus and “green” having a wide distribution[21]. (3) Embedding our representation in a probabilistic framework enables us to apply a wide array of statistical and probabilistic tools to further analyze and study the effect of categories on colors. We implement our model on two datasets. We extract color naming data from six languages in the World Color Survey which contains naming information at 330 colors on the surface of the Munsell solid[7]. We also investigate online naming data collected by DoloresLabs which contains names given to 10,000 randomly sampled colors in the RGB cube[8]. Our framework can incorporate cross-linguistic data and combine contributions from color words with similar meanings. We introduce the concept of salient colors based on the statistical notion of entropy. Salient colors from our approach show good correspondence basic color terms identified by Berlin and Kay[3]. Our approach also reveals two regions that are consistently named in the sRGB cube not corresponding to typical basic color terms. We compare qualitatively the differences in salient name regions between the World Color Survey and the DoloresLabs datasets. Motivations and Related Work The goal of this paper is to present a computational framework for modeling color categories derived from experimental data. Our framework is motivated by three issues that are at best partially addressed in the current literature. 1. We would like a framework that can include all possible words for describing a color and not be limited to a predefined list of terms. 2. We would like a non-parametric model capable of capturing the details in categorical association but still be robust to noise in the naming dataset. 3. We would like a framework that can support a rich set of computational and mathematical operations, so that more in-depth studies of categorical effects can be built on the framework. In particular, our approach is grounded in probability theory. The first issue addresses how to account for the many potential expressions for describing a color. In 1969, Berlin and Kay defined color words as basic color terms if their meanings cannot be derived from other words, and proposed that there are a total of eleven basic color terms. Basic color terms were shown to be universal across languages. While some languages such as English contain all eleven terms, others may have developed only a subset of the words[3]. Subsequent studies confirmed that basic color terms are words with the highest consensus between speakers[4], but found twelve basic color terms in Russian contradicting the limit on the number of terms[22]. Kay and McDaniel hypothesized that as languages evolve, some individuals may consider additional words such as aqua/turquoise (green and blue), chartreuse/lime (yellow and green), and maroon/burgundy (red and black) as basic color terms[9]. Many existing methods assume eleven or a fixed number of color categories and cannot process the full set of responses from recent surveys such as the HP Labs Multilingual Naming Experiment[18] and the DoloresLabs Naming Dataset[8], which have hundreds of color words. Chang et al.’s category-preserving color transfer algorithm defines eleven convex regions in the color space corresponding to the basic color terms[5]. Motomura’s categorical color mapping algorithm maps foci of the eight chromatic basic color terms between the source and target gamuts[19]. Moroney’s system for translating colors to names operates on the n most frequently used color words. We want a framework where all words are included and contributions from words with similar cognitive concepts such as “maroon” and “burgundy” are combined based on their similarity. Secondly, color names exhibit different naming distributions. Colors such as “red” and “yellow” are known to have a narrow and well-defined center while colors such as “green” and “blue” are known to be composed of a broad range of hue.[21] We want our framework have the flexibility to capture the details in the distributions while being robust to noise in the data. Current approaches tend to model color categories as a volume in color space, using various parameterized models, or using non-parameterized approaches such as histograms. Partitioning the color space[12, 5] assume color names occupy discrete and non-overlapping regions in the color space. Motomura’s gamut-mapping algorithm assumes that each basic term has an ellipsoid-shaped distribution and models the distributions using an 81-parameter covariance matrix[19]. Benavente models the color naming space using a set of 6-parameter SigmoidGaussian distributions[1]. One advantage of parameterized models is that they are constructed from a small number of parameters which can be estimated accurately. In his adaptive lexical classification system, Moroney proposes an alternative implementation in which color names are represented as non-parametric histograms[16]. While histograms can capture any shape of distribution, Moroney reported noise in the data due to limited number of data points and suggests that smoothing operators or hedging be applied to post-process the histograms.1 Finally, we would like a framework capable of supporting a rich set of computational and mathematical tools. Instead of being merely a representation, the framework should allows us to perform further computation and analysis on how categories affect the way we associate colors. Treating the association between colors as a probability distribution positions our framework within the well-studied domain of probability theory. Methodology Colors and Color Words A naming dataset consists of a list of responses in the form of “color”-“color word” pairs that record the words used to describe a color. A “color” refers to the stimuli shown to a respondent and varies between datasets from Munsell color chips viewed under controlled lighting to rectangles of colors displayed on uncalibrated monitors. Unconstrained surveys allow respondents to use any expression whereas constrained surveys ask respondents to choose from a predefined list of words. An unconstrained color expression could include, e.g., “granny smith apple green”, “light robin’s egg pastel blue”, or “mix all the paint together”. In practice, most expressions recorded in unconstrained surveys consistent of a single word or a simple set of words such as “blue” or 1We should emphasize our application differs from Moroney’s in that his work is on modeling the distribution of color names while our work is on modeling the association between colors due to naming effects. “bluish green”. We will use the term “color words” from this point on even though it could refer to any possible expressions for describing a color. A naming dataset can be tabulated using a word count table where the list of all colors presented in the survey is displayed along the columns, and a list of all color words recorded is displayed along the rows. Each entry in the table indicates the number of times a corresponding color word is used to describe the corresponding color. Depending on the nature of the naming dataset, the density of word count table may vary. The World Color Survey (WCS)[7] is cross-linguistic and unconstrained, and collects naming data on a set of 330 colors. The word count table for the WCS consisting of 2300 rows by 330 columns with 20% non-zero entries. In comparison, the DoloresLabs color name dataset[8] while also unconstrained uses 10000 randomly-sampled colors. A total of 1966 expressi

/pdf/a-probabilistic-model-of-the-categorical-association-between-31l7czjbe2.pdf

A Probabilistic Model of the Categorical Association Between Colors.

Typical stereo displays stimulate incorrect focus cues because the light comes from a single surface. The consequences of incorrect focus cues include discomfort, difficulty perceiving the depth of stereo images, and incorrect perception of distance. This thesis describes a prototype stereo display comprising two independent volumetric displays. Each volumetric display is implemented with three image planes, which are fixed in position relative to the viewer, and are arranged so that the retinal image is the sum of light from three different focal distances. Scene geometry is rendered separately to the two fixed-viewpoint volumetric displays, using projections that are exact for each eye. Rendering is filtered in the depth dimension such that object radiance is apportioned to the two nearest image planes, in linear proportion based on reciprocal distance. 
Fixed-viewpoint volumetric displays with adequate depth resolution are shown to generate near-correct stimulation of focus cues. Depth filtering, which is necessary to avoid visible artifacts, also improves the accuracy of the stimulus that directs changes in the focus of the eye. Specifically, the stimulus generated by depth filtering an object whose simulated distance falls between image-plane distances closely matches the stimulus that would be generated by an image plane at the desired distance. Viewers of the prototype display required substantially less time to perceive the depth of stereo images that were rendered with depth filtering to approximate correct focal distance. 
Fixed-viewpoint volumetric displays are shown to be a potentially practical solution for virtual reality viewing. In addition to near-correct stimulation of focus cues, and unlike more familiar autostereoscopic volumetric displays, fixed-viewpoint volumetric displays retain important qualities of 3-D projective graphics. These include correct depiction of occlusions and reflections, utilization of modern graphics processor and 2-D display technology, and implementation of realistic fields of view and depths of field. The design and verification of the prototype display are fully described. While not a practical solution for general-purpose viewing, the prototype is a proof of concept and a platform for ongoing vision research.

/pdf/achieving-near-correct-focus-cues-using-multiple-image-1pxyv2qqhr.pdf

Achieving near-correct focus cues using multiple image planes

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