Spatial regression and spillover effects in cluster randomized trials with count outcomes.

This paper describes methodology for analyzing data from cluster randomized trials with count outcomes, taking indirect effects as well spatial effects into account. Indirect effects are modeled using a novel application of a measure of depth within the intervention arm. Both direct and indirect effects can be estimated accurately even when the proposed model is misspecified. We use spatial regression models with Gaussian random effects, where the individual outcomes have distributions overdispersed with respect to the Poisson, and the corresponding direct and indirect effects have a marginal interpretation. To avoid spatial confounding, we use orthogonal regression, in which random effects represent spatial dependence using a homoscedastic and dimensionally reduced modification of the intrinsic conditional autoregression model. We illustrate the methodology using spatial data from a pair-matched cluster randomized trial against the dengue mosquito vector Aedes aegypti, done in Trujillo, Venezuela

Visual saliency computation for image analysis

Visual saliency computation is about detecting and understanding salient regions and elements in a visual scene. Algorithms for visual saliency computation can give clues to where people will look in images, what objects are visually prominent in a scene, etc. Such algorithms could be useful in a wide range of applications in computer vision and graphics. In this thesis, we study the following visual saliency computation problems. 1) Eye Fixation Prediction. Eye fixation prediction aims to predict where people look in a visual scene. For this problem, we propose a Boolean Map Saliency (BMS) model which leverages the global surroundedness cue using a Boolean map representation. We draw a theoretic connection between BMS and the Minimum Barrier Distance (MBD) transform to provide insight into our algorithm. Experiment results show that BMS compares favorably with state-of-the-art methods on seven benchmark datasets. 2) Salient Region Detection. Salient region detection entails computing a saliency map that highlights the regions of dominant objects in a scene. We propose a salient region detection method based on the Minimum Barrier Distance (MBD) transform. We present a fast approximate MBD transform algorithm with an error bound analysis. Powered by this fast MBD transform algorithm, our method can run at about 80 FPS and achieve state-of-the-art performance on four benchmark datasets. 3) Salient Object Detection. Salient object detection targets at localizing each salient object instance in an image. We propose a method using a Convolutional Neural Network (CNN) model for proposal generation and a novel subset optimization formulation for bounding box filtering. In experiments, our subset optimization formulation consistently outperforms heuristic bounding box filtering baselines, such as Non-maximum Suppression, and our method substantially outperforms previous methods on three challenging datasets. 4) Salient Object Subitizing. We propose a new visual saliency computation task, called Salient Object Subitizing, which is to predict the existence and the number of salient objects in an image using holistic cues. To this end, we present an image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace. We show that an end-to-end trained CNN subitizing model can achieve promising performance without requiring any localization process. A method is proposed to further improve the training of the CNN subitizing model by leveraging synthetic images. 5) Top-down Saliency Detection. Unlike the aforementioned tasks, top-down saliency detection entails generating task-specific saliency maps. We propose a weakly supervised top-down saliency detection approach by modeling the top-down attention of a CNN image classifier. We propose Excitation Backprop and the concept of contrastive attention to generate highly discriminative top-down saliency maps. Our top-down saliency detection method achieves superior performance in weakly supervised localization tasks on challenging datasets. The usefulness of our method is further validated in the text-to-region association task, where our method provides state-of-the-art performance using only weakly labeled web images for training

Color and material perception: Achievements and challenges

There is a large literature characterizing human perception of the lightness and color of matte surfaces arranged in coplanar arrays. In the past ten years researchers have begun to examine perception of lightness and color using wider ranges of stimuli intended to better approximate the conditions of everyday viewing. One emerging line of research concerns perception of lightness and color in scenes that approximate the three-dimensional environment we live in, with objects that need not be matte or coplanar and with geometrically complex illumination. A second concerns the perception of material surface properties other than color and lightness, such as gloss or roughness. This special issue features papers that address the rich set of questions and approaches that have emerged from these new research directions. Here, we briefly describe the articles in the issue and their relation to previous work. Keywords: color perception, material perception Citation: Maloney, L. T., & Brainard, D. H. (2010). Color and material perception: Achievements and challenges. Journal of Vision, 10(9):19, 1-6, http://www.journalofvision.org/content/10/9/19, doi:10.1167/10.9.19. Introduction The classic perceptual correlates of object surface properties are lightness and color. Although there are notable exceptions (e.g., Over the past decade, research has increasingly begun to focus on two rich generalizations of the classic "flatmatte" paradigm (for reviews, see In 2004, we edited a special issue in the Journal of Vision entitled "Perception of color and material properties in complex scenes" Characterizing, estimating, and discriminating the light field One of the most important themes in color and material perception is the role of scene illumination. In flat-matte scenes, the placement and directionality of light sources was little emphasized Journal of Vision Complex light fields and surface color/lightness perception In parallel with direct assessment of the perception of the light field, the past decade has seen a slew of papers that study how the visual system achieves color and lightness constancy in the context of spatially complex light fields (e.g., Along these lines in the current issue, Radonjić, Todorović, and Gilchrist (2010) examine surface lightness perception in three-dimensional scenes with directional lighting and show how grouping principles such as adjacency and surroundedness can help organize the empirical phenomena. Olkkonen, Witzel, Hansen, and Gegenfurtner (2010) study color categorization for real surfaces and daylight illuminants. An entire room with controlled illumination served as the laboratory. They find that color categorization was little changed by marked changes in daylight illumination. Surface material perception: Gloss, roughness A very active area of research is the assessment of lighting and environmental conditions that affect the perception of material properties such as gloss and roughness, and how these properties interact with the perception of color and lightness. Important early work includes Surface material perception is represented by a number of papers in this issue; Kim and Anderson Interactions There are several studies that examined interactions among different material properties. In this issue, Giesel and Gegenfurtner (2010) systematically investigate color perception for real objects made of different materials varying in roughness and gloss from smooth and glossy to matte and corrugated. They show that hue is quite stable across their manipulations, but that other attributes interact. Olkkonen and Brainard (2010) study how changes in real-world illumination affect perceived glossiness and lightness with emphasis on testing independence principles. They show, for example, that the effect of geometric changes in the light field on perceived glossiness is independent of the diffuse reflectance component of the surfaces. Novel themes A number of papers in the current issue introduce novel themes. Wolfe and Myers (2010) examine visual search performance when targets and distractors are characterized by surface material. They find that, although it may be easy to discriminate "fur" or "stone," searching for a patch of fur among the stones is difficult and time-consuming. Visual search based on material differences is inefficient. Motoyoshi (2010) examines how the relationship between highlights and shading triggers perception of translucency and transparency. Goddard, Solomon, and Colin (2010) study the adaptable neural mechanisms responsible for surface color constancy. Boyaci, Fang, Murray, and Kersten (2010) also consider mechanism. They report behavioral results showing how lightness across occlusion depends on spatially distant image features and show (using brain imaging) that human early visual cortex responds strongly to occlusion-dependent lightness variations. They conclude that early cortical processing of lightness is affected by three-dimensional scene interpretation

Fuzzy directional enlacement landscapes

International audienceSpatial relations between objects represented in images are of high importance in various application domains related to pattern recognition and computer vision. By definition, most relations are vague, ambiguous and difficult to formalize precisely by humans. The issue of describing complex spatial configurations, where objects can be imbri-cated in each other, is addressed in this article. A novel spatial relation, called enlacement, is presented and designed using a directional fuzzy landscape approach. We propose a generic fuzzy model that allows to visualize and evaluate complex enlacement configurations between crisp objects, with directional granularity. The interest and the behavior of this approach is highlighted on several characteristic examples

Quantifying the complexity and similarity of chess openings using online chess community data

Hundreds of years after its creation, the game of chess is still widely played worldwide. Opening Theory is one of the pillars of chess and requires years of study to be mastered. Here we exploit the "wisdom of the crowd" in an online chess platform to answer questions that, traditionally, only chess experts could tackle. We first define the relatedness network of chess openings that quantifies how similar two openings are to play. In this network, we spot communities of nodes corresponding to the most common opening choices and their mutual relationships, information which is hard to obtain from the existing classification of openings. Moreover, we use the relatedness network to forecast the future openings players will start to play and we back-test these predictions, obtaining performances considerably higher than those of a random predictor. Finally, we use the Economic Fitness and Complexity algorithm to measure how difficult to play openings are and how skilled in openings players are. This study not only gives a new perspective on chess analysis but also opens the possibility of suggesting personalized opening recommendations using complex network theory