9,883 research outputs found
Sparse Radial Sampling LBP for Writer Identification
In this paper we present the use of Sparse Radial Sampling Local Binary
Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture
classification. By adapting and extending the standard LBP operator to the
particularities of text we get a generic text-as-texture classification scheme
and apply it to writer identification. In experiments on CVL and ICDAR 2013
datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA)
performance. Among the SOA, the proposed method is the only one that is based
on dense extraction of a single local feature descriptor. This makes it fast
and applicable at the earliest stages in a DIA pipeline without the need for
segmentation, binarization, or extraction of multiple features.Comment: Submitted to the 13th International Conference on Document Analysis
and Recognition (ICDAR 2015
2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA
We present a new approach for face recognition system. The method is based on
2D face image features using subset of non-correlated and Orthogonal Gabor
Filters instead of using the whole Gabor Filter Bank, then compressing the
output feature vector using Linear Discriminant Analysis (LDA). The face image
has been enhanced using multi stage image processing technique to normalize it
and compensate for illumination variation. Experimental results show that the
proposed system is effective for both dimension reduction and good recognition
performance when compared to the complete Gabor filter bank. The system has
been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and
achieved average recognition rate of 98.9 %
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks
MirBot is a collaborative application for smartphones that allows users to
perform object recognition. This app can be used to take a photograph of an
object, select the region of interest and obtain the most likely class (dog,
chair, etc.) by means of similarity search using features extracted from a
convolutional neural network (CNN). The answers provided by the system can be
validated by the user so as to improve the results for future queries. All the
images are stored together with a series of metadata, thus enabling a
multimodal incremental dataset labeled with synset identifiers from the WordNet
ontology. This dataset grows continuously thanks to the users' feedback, and is
publicly available for research. This work details the MirBot object
recognition system, analyzes the statistics gathered after more than four years
of usage, describes the image classification methodology, and performs an
exhaustive evaluation using handcrafted features, convolutional neural codes
and different transfer learning techniques. After comparing various models and
transformation methods, the results show that the CNN features maintain the
accuracy of MirBot constant over time, despite the increasing number of new
classes. The app is freely available at the Apple and Google Play stores.Comment: Accepted in Neurocomputing, 201
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