5,694 research outputs found
Texture Segmentation by Evidence Gathering
A new approach to texture segmentation is presented which uses Local Binary Pattern data to provide evidence from which pixels can be classified into texture classes. The proposed algorithm, which we contend to be the first use of evidence gathering in the field of texture classification, uses Generalised Hough Transform style R-tables as unique descriptors for each texture class and an accumulator is used to store votes for each texture class. Tests on the Brodatz database and Berkeley Segmentation Dataset have shown that our algorithm provides excellent results; an average of 86.9% was achieved over 50 tests on 27 Brodatz textures compared with 80.3% achieved by segmentation by histogram comparison centred on each pixel. In addition, our results provide noticeably smoother texture boundaries and reduced noise within texture regions. The concept is also a "higher order" texture descriptor, whereby the arrangement of texture elements is used for classification as well as the frequency of occurrence that is featured in standard texture operators. This results in a unique descriptor for each texture class based on the structure of texture elements within the image, which leads to a homogeneous segmentation, in boundary and area, of texture by this new technique
The image ray transform for structural feature detection
The use of analogies to physical phenomena is an exciting paradigm in computer vision that allows unorthodox approaches to feature extraction, creating new techniques with unique properties. A technique known as the "image ray transform" has been developed based upon an analogy to the propagation of light as rays. The transform analogises an image to a set of glass blocks with refractive index linked to pixel properties and then casts a large number of rays through the image. The course of these rays is accumulated into an output image. The technique can successfully extract tubular and circular features and we show successful circle detection, ear biometrics and retinal vessel extraction. The transform has also been extended through the use of multiple rays arranged as a beam to increase robustness to noise, and we show quantitative results for fully automatic ear recognition, achieving 95.2% rank one recognition across 63 subjects
Connectivity-Enforcing Hough Transform for the Robust Extraction of Line Segments
Global voting schemes based on the Hough transform (HT) have been widely used
to robustly detect lines in images. However, since the votes do not take line
connectivity into account, these methods do not deal well with cluttered
images. In opposition, the so-called local methods enforce connectivity but
lack robustness to deal with challenging situations that occur in many
realistic scenarios, e.g., when line segments cross or when long segments are
corrupted. In this paper, we address the critical limitations of the HT as a
line segment extractor by incorporating connectivity in the voting process.
This is done by only accounting for the contributions of edge points lying in
increasingly larger neighborhoods and whose position and directional content
agree with potential line segments. As a result, our method, which we call
STRAIGHT (Segment exTRAction by connectivity-enforcInG HT), extracts the
longest connected segments in each location of the image, thus also integrating
into the HT voting process the usually separate step of individual segment
extraction. The usage of the Hough space mapping and a corresponding
hierarchical implementation make our approach computationally feasible. We
present experiments that illustrate, with synthetic and real images, how
STRAIGHT succeeds in extracting complete segments in several situations where
current methods fail.Comment: Submitted for publicatio
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities
Current object recognition methods fail on object sets that include both
diffuse, reflective and transparent materials, although they are very common in
domestic scenarios. We show that a combination of cues from multiple sensor
modalities, including specular reflectance and unavailable depth information,
allows us to capture a larger subset of household objects by extending a state
of the art object recognition method. This leads to a significant increase in
robustness of recognition over a larger set of commonly used objects.Comment: 12 page
Handwritten digit classification
Pattern recognition is one of the major challenges in statistics framework. Its goal is the feature extraction to classify the patterns into categories. A well-known example in this field is the handwritten digit recognition where digits have to be assigned into one of the 10 classes using some classification method. Our purpose is to present alternative classification methods based on statistical techniques. We show a comparison between a multivariate and a probabilistic approach, concluding that both methods provide similar results in terms of test-error rate. Experiments are performed on the known MNIST and USPS databases in binary-level image. Then, as an additional contribution we introduce a novel method to binarize images, based on statistical concepts associated to the written trace of the digitDigit, Classification, Images
An Event-Driven Multi-Kernel Convolution Processor Module for Event-Driven Vision Sensors
Event-Driven vision sensing is a new way of sensing
visual reality in a frame-free manner. This is, the vision sensor
(camera) is not capturing a sequence of still frames, as in conventional
video and computer vision systems. In Event-Driven sensors
each pixel autonomously and asynchronously decides when to
send its address out. This way, the sensor output is a continuous
stream of address events representing reality dynamically continuously
and without constraining to frames. In this paper we present
an Event-Driven Convolution Module for computing 2D convolutions
on such event streams. The Convolution Module has been
designed to assemble many of them for building modular and hierarchical
Convolutional Neural Networks for robust shape and
pose invariant object recognition. The Convolution Module has
multi-kernel capability. This is, it will select the convolution kernel
depending on the origin of the event. A proof-of-concept test prototype
has been fabricated in a 0.35 m CMOS process and extensive
experimental results are provided. The Convolution Processor has
also been combined with an Event-Driven Dynamic Vision Sensor
(DVS) for high-speed recognition examples. The chip can discriminate
propellers rotating at 2 k revolutions per second, detect symbols
on a 52 card deck when browsing all cards in 410 ms, or detect
and follow the center of a phosphor oscilloscope trace rotating at
5 KHz.Unión Europea 216777 (NABAB)Ministerio de Ciencia e Innovación TEC2009-10639-C04-0
An Extension to Hough Transform Based on Gradient Orientation
The Hough transform is one of the most common methods for line detection. In
this paper we propose a novel extension of the regular Hough transform. The
proposed extension combines the extension of the accumulator space and the
local gradient orientation resulting in clutter reduction and yielding more
prominent peaks, thus enabling better line identification. We demonstrate
benefits in applications such as visual quality inspection and rectangle
detection.Comment: Part of the Proceedings of the Croatian Computer Vision Workshop,
CCVW 2015, Year
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