6 research outputs found
Vector quantizing feature space with a regular lattice
International audienceMost recent class-level object recognition systems work with visual words, i.e., vector quantized local descriptors. In this paper we examine the feasibility of a data- independent approach to construct such a visual vocabulary, where the feature space is discretized using a regular lattice. Using hashing techniques, only non-empty bins are stored, and fine-grained grids become possible in spite of the high dimensionality of typical feature spaces. Based on this representation, we can explore the structure of the feature space, and obtain state-of-the-art pixelwise classification results. In the case of image classification, we introduce a class-specific feature selection step, which takes the spatial structure of SIFT-like descriptors into account. Results are reported on the Graz02 dataset
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Contour and texture for visual recognition of object categories
The recognition of categories of objects in images has become a central
topic in computer vision. Automatic visual recognition systems
are rapidly becoming central to applications such as image search,
robotics, vehicle safety systems, and image editing. This work addresses
three sub-problems of recognition: image classification, object
detection, and semantic segmentation. The task of classification
is to determine whether an object of a particular category is present
or not. Object detection aims to localize any objects of the category.
Semantic segmentation is a more complete image understanding,
whereby an image is partitioned into coherent regions that are assigned
meaningful class labels. This thesis proposes novel discriminative
learning approaches to these problems.
Our primary contributions are threefold. Firstly, we demonstrate
that the contours (the outline and interior edges) of an object are,
alone, sufficient for accurate visual recognition. Secondly, we propose
two powerful new feature types: (i) a learned codebook of contour
fragments matched with an improved oriented chamfer distance,
and (ii) a set of texture-based features that simultaneously exploit
local appearance, approximate shape, and appearance context.
The efficacy of these new features types is evaluated on a wide variety
of datasets. Thirdly, we show how, in combination, these two
largely orthogonal feature types can substantially improve recognition
performance above that achieved by either alone
Statistical cues for domain specific image segmentation with performance analysis
This paper investigates the use of colour and texture cues for segmentation of images within two specified domains. The first is the Sowerby dataset, which contains one hundred colour photographs of country roads in England that have been interactively segmented and classified into six classes – edge, vegetation, air, road, building, and other. The second domain is a set of thirty five images, taken in San Francisco, which have been interactively segmented into similar classes. In each domain we learn the joint probability distributions of filter responses, based on colour and texture, for each class. These distributions are then used for classification. We restrict ourselves to a limited number of filters in order to ensure that the learnt filter responses do not overfit the training data (our region classes are chosen so as to ensure that there is enough data to avoid overfitting). We do performance analysis on the two datasets by evaluating the false positive and false negative error rates for the classification. This shows that the learnt models achieve high accuracy in classifying individual pixels into those classes for which the filter responses are approximately spatially homogeneous (i.e. road, vegetation, and air but not edge and building). A more sensitive performance measure, the Chernoff information, is calculated in order to quantify how well the cues for edge and building are doing. This demonstrates that statistical knowledge of the domain is a powerful tool for segmentation
Context-driven Object Detection and Segmentation with Auxiliary Information
One fundamental problem in computer vision and robotics is to
localize objects of interest in an image. The task can either be
formulated as an object detection problem if the objects are
described by a set of pose parameters, or an object segmentation
one if we recover object boundary precisely. A key issue in
object detection and segmentation concerns exploiting the spatial
context, as local evidence is often insufficient to determine
object pose in the presence of heavy occlusions or large object
appearance variations. This thesis addresses the object detection
and segmentation problem in such adverse conditions with
auxiliary depth data provided by RGBD cameras. We focus on four
main issues in context-aware object detection and segmentation:
1) what are the effective context representations? 2) how can we
work with limited and imperfect depth data? 3) how to design
depth-aware features and integrate depth cues into conventional
visual inference tasks? 4) how to make use of unlabeled data to
relax the labeling requirements for training data?
We discuss three object detection and segmentation scenarios
based on varying amounts of available auxiliary information. In
the first case, depth data are available for model training but
not available for testing. We propose a structured Hough voting
method for detecting objects with heavy occlusion in indoor
environments, in which we extend the Hough hypothesis space to
include both the object's location, and its visibility pattern.
We design a new score function that accumulates votes for object
detection and occlusion prediction. In addition, we explore the
correlation between objects and their environment, building a
depth-encoded object-context model based on RGBD data. In the
second case, we address the problem of localizing glass objects
with noisy and incomplete depth data. Our method integrates the
intensity and depth information from a single view point, and
builds a Markov Random Field that predicts glass boundary and
region jointly. In addition, we propose a nonparametric,
data-driven label transfer scheme for local glass boundary
estimation. A weighted voting scheme based on a joint feature
manifold is adopted to integrate depth and appearance cues, and
we learn a distance metric on the depth-encoded feature manifold.
In the third case, we make use of unlabeled data to relax the
annotation requirements for object detection and segmentation,
and propose a novel data-dependent margin distribution learning
criterion for boosting, which utilizes the intrinsic geometric
structure of datasets. One key aspect of this method is that it
can seamlessly incorporate unlabeled data by including a graph
Laplacian regularizer. We demonstrate the performance of our
models and compare with baseline methods on several real-world
object detection and segmentation tasks, including indoor object
detection, glass object segmentation and foreground segmentation
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