63,527 research outputs found
SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction
Cell nuclei detection and fine-grained classification have been fundamental
yet challenging problems in histopathology image analysis. Due to the nuclei
tiny size, significant inter-/intra-class variances, as well as the inferior
image quality, previous automated methods would easily suffer from limited
accuracy and robustness. In the meanwhile, existing approaches usually deal
with these two tasks independently, which would neglect the close relatedness
of them. In this paper, we present a novel method of sibling fully
convolutional network with prior objectness interaction (called SFCN-OPI) to
tackle the two tasks simultaneously and interactively using a unified
end-to-end framework. Specifically, the sibling FCN branches share features in
earlier layers while holding respective higher layers for specific tasks. More
importantly, the detection branch outputs the objectness prior which
dynamically interacts with the fine-grained classification sibling branch
during the training and testing processes. With this mechanism, the
fine-grained classification successfully focuses on regions with high
confidence of nuclei existence and outputs the conditional probability, which
in turn benefits the detection through back propagation. Extensive experiments
on colon cancer histology images have validated the effectiveness of our
proposed SFCN-OPI and our method has outperformed the state-of-the-art methods
by a large margin.Comment: Accepted at AAAI 201
View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network
This paper describes the details of Sighthound's fully automated vehicle
make, model and color recognition system. The backbone of our system is a deep
convolutional neural network that is not only computationally inexpensive, but
also provides state-of-the-art results on several competitive benchmarks.
Additionally, our deep network is trained on a large dataset of several million
images which are labeled through a semi-automated process. Finally we test our
system on several public datasets as well as our own internal test dataset. Our
results show that we outperform other methods on all benchmarks by significant
margins. Our model is available to developers through the Sighthound Cloud API
at https://www.sighthound.com/products/cloudComment: 7 Page
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
TThe goal of our work is to discover dominant objects in a very general
setting where only a single unlabeled image is given. This is far more
challenge than typical co-localization or weakly-supervised localization tasks.
To tackle this problem, we propose a simple but effective pattern mining-based
method, called Object Location Mining (OLM), which exploits the advantages of
data mining and feature representation of pre-trained convolutional neural
networks (CNNs). Specifically, we first convert the feature maps from a
pre-trained CNN model into a set of transactions, and then discovers frequent
patterns from transaction database through pattern mining techniques. We
observe that those discovered patterns, i.e., co-occurrence highlighted
regions, typically hold appearance and spatial consistency. Motivated by this
observation, we can easily discover and localize possible objects by merging
relevant meaningful patterns. Extensive experiments on a variety of benchmarks
demonstrate that OLM achieves competitive localization performance compared
with the state-of-the-art methods. We also evaluate our approach compared with
unsupervised saliency detection methods and achieves competitive results on
seven benchmark datasets. Moreover, we conduct experiments on fine-grained
classification to show that our proposed method can locate the entire object
and parts accurately, which can benefit to improving the classification results
significantly
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