2,049 research outputs found
Multi-view Convolutional Neural Networks for 3D Shape Recognition
A longstanding question in computer vision concerns the representation of 3D
shapes for recognition: should 3D shapes be represented with descriptors
operating on their native 3D formats, such as voxel grid or polygon mesh, or
can they be effectively represented with view-based descriptors? We address
this question in the context of learning to recognize 3D shapes from a
collection of their rendered views on 2D images. We first present a standard
CNN architecture trained to recognize the shapes' rendered views independently
of each other, and show that a 3D shape can be recognized even from a single
view at an accuracy far higher than using state-of-the-art 3D shape
descriptors. Recognition rates further increase when multiple views of the
shapes are provided. In addition, we present a novel CNN architecture that
combines information from multiple views of a 3D shape into a single and
compact shape descriptor offering even better recognition performance. The same
architecture can be applied to accurately recognize human hand-drawn sketches
of shapes. We conclude that a collection of 2D views can be highly informative
for 3D shape recognition and is amenable to emerging CNN architectures and
their derivatives.Comment: v1: Initial version. v2: An updated ModelNet40 training/test split is
used; results with low-rank Mahalanobis metric learning are added. v3 (ICCV
2015): A second camera setup without the upright orientation assumption is
added; some accuracy and mAP numbers are changed slightly because a small
issue in mesh rendering related to specularities is fixe
A Novel Scheme for Intelligent Recognition of Pornographic Images
Harmful contents are rising in internet day by day and this motivates the
essence of more research in fast and reliable obscene and immoral material
filtering. Pornographic image recognition is an important component in each
filtering system. In this paper, a new approach for detecting pornographic
images is introduced. In this approach, two new features are suggested. These
two features in combination with other simple traditional features provide
decent difference between porn and non-porn images. In addition, we applied
fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron)
and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of
system was evaluated over 18354 download images from internet. The attained
precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on
test dataset. Achieved results verify the performance of proposed system versus
other related works
Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language
A good Arabic handwritten recognition system must consider the characteristics of Arabic letters which can be explicit such as the presence of diacritics or implicit such as the baseline information (a virtual line on which cursive text are aligned and/join). In order to find an adequate method of features extraction, we have taken into consideration the nature of the Arabic characters. The paper investigate two methods based on two different visions: one describes the image in terms of the distribution of pixels, and the other describes it in terms of local patterns. Spatial Distribution of Pixels (SDP) is used according to the first vision; whereas Local Binary Patterns (LBP) are used for the second one. Tested on the Arabic portion of the Isolated Farsi Handwritten Character Database (IFHCDB) and using neural networks as a classifier, SDP achieve a recognition rate around 94% while LBP achieve a recognition rate of about 96%
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