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Many tasks in computer vision and graphics fall within the framework of conditional image synthesis. In recent years, generative adversarial nets have delivered impressive advances in quality of synthesized images. However, it remains a challenge to generate both diverse and plausible images for the same input, due to the problem of mode collapse. In this paper, we develop a new generic multimodal conditional image synthesis method based on implicit maximum likelihood estimation and demonstrate improved multimodal image synthesis performance on two tasks, single image super-resolution and image synthesis from scene layouts. We make our implementation publicly available. [ABSTRACT FROM AUTHOR]

We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: (a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the task, and (b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. We train and test CMP on navigation problems in simulation environments derived from scans of real world buildings. Our experiments demonstrate that CMP outperforms alternate learning-based architectures, as well as, classical mapping and path planning approaches in many cases. Furthermore, it naturally extends to semantically specified goals, such as "going to a chair". We also deploy CMP on physical robots in indoor environments, where it achieves reasonable performance, even though it is trained entirely in simulation. [ABSTRACT FROM AUTHOR]

In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (Silberman et al., ECCV, ). We propose algorithms for object boundary detection and hierarchical segmentation that generalize the $$gPb-ucm$$ approach of Arbelaez et al. (TPAMI, ) by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We train RGB-D object detectors by analyzing and computing histogram of oriented gradients on the depth image and using them with deformable part models (Felzenszwalb et al., TPAMI, ). We observe that this simple strategy for training object detectors significantly outperforms more complicated models in the literature. We then turn to the problem of semantic segmentation for which we propose an approach that classifies superpixels into the dominant object categories in the NYUD2 dataset. We design generic and class-specific features to encode the appearance and geometry of objects. We also show that additional features computed from RGB-D object detectors and scene classifiers further improves semantic segmentation accuracy. In all of these tasks, we report significant improvements over the state-of-the-art. [ABSTRACT FROM AUTHOR]

Using a large set of human segmented natural images, we study the statistics of region boundaries. We observe several power law distributions which likely arise from both multi-scale structure within individual objects and from arbitrary viewing distance. Accordingly, we develop a scale-invariant representation of images from the bottom up, using a piecewise linear approximation of contours and constrained Delaunay triangulation to complete gaps. We model curvilinear grouping on top of this graphical/geometric structure using a conditional random field to capture the statistics of continuity and different junction types. Quantitative evaluations on several large datasets show that our contour grouping algorithm consistently dominates and significantly improves on local edge detection. [ABSTRACT FROM AUTHOR]

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods. [ABSTRACT FROM AUTHOR]

This paper demonstrates a new visual motion estimation technique that is able to recover high degree-of-freedom articulated human body configurations in complex video sequences. We introduce the use and integration of a mathematical technique, the product of exponential maps and twist motions, into a differential motion estimation. This results in solving simple linear systems, and enables us to recover robustly the kinematic degrees-of-freedom in noise and complex self occluded configurations. A new factorization technique lets us also recover the kinematic chain model itself. We are able to track several human walk cycles, several wallaby hop cycles, and two walk cycels of the famous movements of Eadweard Muybridge's motion studies from the last century. To the best of our knowledge, this is the first computer vision based system that is able to process such challenging footage. [ABSTRACT FROM AUTHOR]

We study the recognition of surfaces made from different materials such as concrete, rug, marble, or leather on the basis of their textural appearance. Such natural textures arise from spatial variation of two surface attributes: (1) reflectance and (2) surface normal. In this paper, we provide a unified model to address both these aspects of natural texture. The main idea is to construct a vocabulary of prototype tiny surface patches with associated local geometric and photometric properties. We call these 3D textons. Examples might be ridges, grooves, spots or stripes or combinations thereof. Associated with each texton is an appearance vector, which characterizes the local irradiance distribution, represented as a set of linear Gaussian derivative filter outputs, under different lighting and viewing conditions. Given a large collection of images of different materials, a clustering approach is used to acquire a small (on the order of 100) 3D texton vocabulary. Given a few (1 to 4) images of any material, it can be characterized using these textons. We demonstrate the application of this representation for recognition of the material viewed under novel lighting and viewing conditions. We also illustrate how the 3D texton model can be used to predict the appearance of materials under novel conditions. [ABSTRACT FROM AUTHOR]

This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown. [ABSTRACT FROM AUTHOR]

Portions of surfaces in a binocularly viewed scene may be 'half occluded', that is, visible in only one eye. The human visual system uses zones of half occlusion to help segment the visual scene and infer figure-ground relationships at object boundaries. We developed a quantitative model of the depthdiscontinuity cue provided by half occlusion. Half occlusions are revealed by two-dimensional interocular displacements of binocularly viewed occlusion junctions, such as T junctions. We derived a formula relating this two-dimensional displacement, or 'pseudodisparity', to binocular disparities and orientations of occluding and occluded contours. In human psychophysical experiments, perceived depth and contour orientation quantitatively depended on pseudodisparity, as predicted by our model, implying that the visual system senses quantitative variations in interocular junction position to reconstruct occlusion geometry. [ABSTRACT FROM AUTHOR]

Shape from texture is best analyzed in two stages, analogous to stereopsis and structure from motion: (a) Computing the ‘texture distortion’ from the image, and (b) Interpreting the ‘texture distortion’ to infer the orientation and shape of the surface in the scene. We model the texture distortion for a given point and direction on the image plane as an affine transformation and derive the relationship between the parameters of this transformation and the shape parameters. We have developed a technique for estimating affine transforms between nearby image patches which is based on solving a system of linear constraints derived from a differential analysis. One need not explicitly identify texels or make restrictive assumptions about the nature of the texture such as isotropy. We use non-linear minimization of a least squares error criterion to recover the surface orientation (slant and tilt) and shape (principal curvatures and directions) based on the estimated affine transforms in a number of different directions. A simple linear algorithm based on singular value decomposition of the linear parts of the affine transforms provides the initial guess for the minimization procedure. Experimental results on both planar and curved surfaces under perspective projection demonstrate good estimates for both orientation and shape. A sensitivity analysis yields predictions for both computer vision algorithms and human perception of shape from texture. [ABSTRACT FROM AUTHOR]

We have developed a new algorithm for computing optical flow in a differential framework. The image sequence is first convolved with a set of linear, separable spatiotemporal filter kernels similar to those that have been used in other early vision problems such as texture and stereopsis. The brightness constancy constraint can then be applied to each of the resulting images, giving us, in general, an overdetermined system of equations for the optical flow at each pixel. There are three principal sources of error: (a) stochastic error due to sensor noise (b) systematic errors in the presence of large displacements and (c) errors due to failure of the brightness constancy model. Our analysis of these errors leads us to develop an algorithm based on a robust version of total least squares. Each optical flow vector computed has an associated reliability measure which can be used in subsequent processing. The performance of the algorithm on the data set used by Barron et al. (IJCV 1994) compares favorably with other techniques. In addition to being separable, the filters used are also causal, incorporating only past time frames. The algorithm is fully parallel and has been implemented on a multiple processor machine. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of interpreting line drawings of scenes composed of opaque regular solid objects bounded by piecewise smooth surfaces with no markings or texture on them. It is assumed that the line drawing has been formed by orthographic projection of such a scene under general viewpoint, that the line drawing is error free, and that there are no lines due to shadows or specularities. Our definition implicitly excludes laminae, wires, and the apices of cones. A major component of the interpretation of line drawings is line labelling. By line labelling we mean (a) classification of each image curve as corresponding to either a depth or orientation discontinuity in the scene, and (b) further subclassification of each kind of discontinuity. For a depth discontinuity we determine whether it is a limb-a locus of points on the surface where the line of sight is tangent to the surface-or an occluding edge-a tangent plane discontinuity of the surface. For an orientation discontinuity, we determine whether it corresponds to a convex or concave edge. This paper presents the first mathematically rigorous scheme for labelling line drawings of the class of scenes described. Previous schemes for labelling line drawings of scenes containing curved objects were heuristic, incomplete, and lacked proper mathematical justification. By analyzing the projection of the neighborhoods of different kinds of points on a piecewise smooth surface, we are able to catalog all local labelling possibilities for the different types of junctions in a line drawing. An algorithm is developed which utilizes this catalog to determine all legal labellings of the line drawing. A local minimum complexity rule-at each vertex select those labellings which correspond to the minimum number of faces meeting at the vertex-is used in order to prune highly counter-intuitive interpretations. The labelling scheme was implemented and tested on a number of line drawings. The labellings obtained are few and by and large in accordance with human interpretations. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to investigate the effects of a single oral dose of monocrotophos on the plasma cholinesterase (ChE), serum aminotransferases, and total plasma proteins of Bubalus bubalis. The study used 12 healthy buffalo calves that were purchased from the local market and were given the monocrotophos, Nuvacron, from Hindustan Ciba-Geigy Ltd. Results show that monocrotophos produced mild to moderate degree of toxic symptoms similar to anticholinesterase poisoning.

The biotransformation and disposition of 3-chloro-4-methyl-(4-C)-7-hydroxycoumarin [(C) chlorferron] were investigated in rats after single oral administration. Following administration of (C) chlorferron at 0.5 and 20 mg/kg body weight to male rats, > 90% of the given dose was eliminated in the urine (77-84%) and faeces (7-15%) within 24 h. Low levels of (C) chlorferron derived residues were detected in different organs of rats 7 days after dosing. Administration of (C) chlorferron at 20 mg/kg allowed the isolation of three metabolites in the 24-h urine of male and female rats. Compounds tentatively identified were dechlorinated metabolites of chlorferron besides unchanged chlorferron. The majority of the metabolites were excreted in conjugated forms. The pattern of biotransformation of chlorferron was qualitatively similar in male and female rats. Comparative studies on the elimination and biodistribution of (C) chlorferron and its parent compound (C) coumaphos in male rats indicated rapid metabolism and faster elimination of chlorferron and its metabolites from the body than that of the parent compound. [ABSTRACT FROM AUTHOR]

The article presents a study on thermoregulatory effect of monocrotophos and antidotal therapy on Bubalus bubalis. The study used the student t-test to analyse the data statistically and the significance was assessed at 0.01 and 0.05 levels. Result shows that monocrotophos developed significant fall in animals body temperature and the insecticide intoxication hypothermia was found to be time and dose dependent.