127,483 research outputs found
Plant image retrieval using color, shape and texture features
We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques
and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered
Object recognition using shape-from-shading
This paper investigates whether surface topography information extracted from intensity images using a recently reported shape-from-shading (SFS) algorithm can be used for the purposes of 3D object recognition. We consider how curvature and shape-index information delivered by this algorithm can be used to recognize objects based on their surface topography. We explore two contrasting object recognition strategies. The first of these is based on a low-level attribute summary and uses histograms of curvature and orientation measurements. The second approach is based on the structural arrangement of constant shape-index maximal patches and their associated region attributes. We show that region curvedness and a string ordering of the regions according to size provides recognition accuracy of about 96 percent. By polling various recognition schemes. including a graph matching method. we show that a recognition rate of 98-99 percent is achievable
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching
This paper proposes a content based image retrieval (CBIR) system using the
local colour and texture features of selected image sub-blocks and global
colour and shape features of the image. The image sub-blocks are roughly
identified by segmenting the image into partitions of different configuration,
finding the edge density in each partition using edge thresholding followed by
morphological dilation. The colour and texture features of the identified
regions are computed from the histograms of the quantized HSV colour space and
Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture
feature vectors is computed for each region. The shape features are computed
from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching
(IRM) algorithm is used for finding the minimum distance between the sub-blocks
of the query and target image. Experimental results show that the proposed
method provides better retrieving result than retrieval using some of the
existing methods.Comment: 7 page
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Survey of Object Detection Methods in Camouflaged Image
Camouflage is an attempt to conceal the signature of a target object into the background image. Camouflage detection
methods or Decamouflaging method is basically used to detect foreground object hidden in the background image. In this
research paper authors presented survey of camouflage detection methods for different applications and areas
The combinatorics of associated Hermite polynomials
We develop a combinatorial model of the associated Hermite polynomials and
their moments, and prove their orthogonality with a sign-reversing involution.
We find combinatorial interpretations of the moments as complete matchings,
connected complete matchings, oscillating tableaux, and rooted maps and show
weight-preserving bijections between these objects. Several identities,
linearization formulas, the moment generating function, and a second
combinatorial model are also derived.Comment: [v1]: 18 pages, 16 figures; presented at FPSAC 2007 [v2]: Some minor
errors fixed (thanks Bill Chen, Jang Soo Kim) and text rearranged and cleaned
up; no real content changes [v3]: fixed typos, to appear in European J.
Combinatoric
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
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