914 research outputs found
Hierarchical Object Parsing from Structured Noisy Point Clouds
Object parsing and segmentation from point clouds are challenging tasks
because the relevant data is available only as thin structures along object
boundaries or other features, and is corrupted by large amounts of noise. To
handle this kind of data, flexible shape models are desired that can accurately
follow the object boundaries. Popular models such as Active Shape and Active
Appearance models lack the necessary flexibility for this task, while recent
approaches such as the Recursive Compositional Models make model
simplifications in order to obtain computational guarantees. This paper
investigates a hierarchical Bayesian model of shape and appearance in a
generative setting. The input data is explained by an object parsing layer,
which is a deformation of a hidden PCA shape model with Gaussian prior. The
paper also introduces a novel efficient inference algorithm that uses informed
data-driven proposals to initialize local searches for the hidden variables.
Applied to the problem of object parsing from structured point clouds such as
edge detection images, the proposed approach obtains state of the art parsing
errors on two standard datasets without using any intensity information.Comment: 13 pages, 16 figure
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A Stochastic Grammar of Images
This exploratory paper quests for a stochastic and context sensitive grammar of images. The grammar should achieve the following four objectives and thus serves as a unified framework of representation, learning, and recognition for a large number of object categories. (i) The grammar represents both the hierarchical decompositions from scenes, to objects, parts, primitives and pixels by terminal and non-terminal nodes and the contexts for spatial and functional relations by horizontal links between the nodes. It formulates each object category as the set of all possible valid configurations produced by the grammar. (ii) The grammar is embodied in a simple And-Or graph representation where each Or-node points to alternative sub-configurations and an And-node is decomposed into a number of components. This representation supports recursive top-down/bottom-up procedures for image parsing under the Bayesian framework and make it convenient to scale up in complexity. Given an input image, the image parsing task constructs a most probable parse graph on-the-fly as the output interpretation and this parse graph is a subgraph of the And-Or graph after making choice on the Or-nodes. (iii) A probabilistic model is defined on this And-Or graph representation to account for the natural occurrence frequency of objects and parts as well as their relations. This model is learned from a relatively small training set per category and then sampled to synthesize a large number of configurations to cover novel object instances in the test set. This generalization capability is mostly missing in discriminative machine learning methods and can largely improve recognition performance in experiments. (iv) To fill the well-known semantic gap between symbols and raw signals, the grammar includes a series of visual dictionaries and organizes them through graph composition. At the bottom-level the dictionary is a set of image primitives each having a number of anchor points with open bonds to link with other primitives. These primitives can be combined to form larger and larger graph structures for parts and objects. The ambiguities in inferring local primitives shall be resolved through top-down computation using larger structures. Finally these primitives forms a primal sketch representation which will generate the input image with every pixels explained. The proposal grammar integrates three prominent representations in the literature: stochastic grammars for composition, Markov (or graphical) models for contexts, and sparse coding with primitives (wavelets). It also combines the structure-based and appearance based methods in the vision literature. Finally the paper presents three case studies to illustrate the proposed grammar.Mathematic
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis
of 3D shapes, particularly their structures. Our key insight is that 3D shapes
are effectively characterized by their hierarchical organization of parts,
which reflects fundamental intra-shape relationships such as adjacency and
symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a
flat, unlabeled, arbitrary part layout to a compact code. The code effectively
captures hierarchical structures of man-made 3D objects of varying structural
complexities despite being fixed-dimensional: an associated decoder maps a code
back to a full hierarchy. The learned bidirectional mapping is further tuned
using an adversarial setup to yield a generative model of plausible structures,
from which novel structures can be sampled. Finally, our structure synthesis
framework is augmented by a second trained module that produces fine-grained
part geometry, conditioned on global and local structural context, leading to a
full generative pipeline for 3D shapes. We demonstrate that without
supervision, our network learns meaningful structural hierarchies adhering to
perceptual grouping principles, produces compact codes which enable
applications such as shape classification and partial matching, and supports
shape synthesis and interpolation with significant variations in topology and
geometry.Comment: Corresponding author: Kai Xu ([email protected]
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
Joint Object and Part Segmentation using Deep Learned Potentials
Segmenting semantic objects from images and parsing them into their
respective semantic parts are fundamental steps towards detailed object
understanding in computer vision. In this paper, we propose a joint solution
that tackles semantic object and part segmentation simultaneously, in which
higher object-level context is provided to guide part segmentation, and more
detailed part-level localization is utilized to refine object segmentation.
Specifically, we first introduce the concept of semantic compositional parts
(SCP) in which similar semantic parts are grouped and shared among different
objects. A two-channel fully convolutional network (FCN) is then trained to
provide the SCP and object potentials at each pixel. At the same time, a
compact set of segments can also be obtained from the SCP predictions of the
network. Given the potentials and the generated segments, in order to explore
long-range context, we finally construct an efficient fully connected
conditional random field (FCRF) to jointly predict the final object and part
labels. Extensive evaluation on three different datasets shows that our
approach can mutually enhance the performance of object and part segmentation,
and outperforms the current state-of-the-art on both tasks
Optimization of facade segmentation based on layout priors
We propose an algorithm that provides a pixel-wise classification of building facades. Building facades provide a rich environment for testing semantic segmentation techniques. They come in a variety of styles affecting appearance and layout. On the other hand, they exhibit a degree of stability in the arrangement of structures across different instances. Furthermore, a single image is often composed of a repetitive architectural pattern. We integrate appearance, layout and repetition cues in a single energy function, that is optimized through the TRW-S algorithm to provide a classification of superpixels. The appearance energy is based on scores of a Random Forrest classifier. The feature space is composed of higher-level vectors encoding distance to structure clusters. Layout priors are obtained from locations and structural adjacencies in training data. In addition, priors result from translational symmetry cues acquired from the scene itself through clustering via the α -expansion graphcut algorithm. We are on par with state-of-the-art. We are able to fine tune classifications at the superpixel level, while most methods model all architectural features with bounding rectangles
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