46,023 research outputs found
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
We propose methodologies to train highly accurate and efficient deep
convolutional neural networks (CNNs) for image super resolution (SR). A cascade
training approach to deep learning is proposed to improve the accuracy of the
neural networks while gradually increasing the number of network layers. Next,
we explore how to improve the SR efficiency by making the network slimmer. Two
methodologies, the one-shot trimming and the cascade trimming, are proposed.
With the cascade trimming, the network's size is gradually reduced layer by
layer, without significant loss on its discriminative ability. Experiments on
benchmark image datasets show that our proposed SR network achieves the
state-of-the-art super resolution accuracy, while being more than 4 times
faster compared to existing deep super resolution networks.Comment: Accepted to IEEE Winter Conf. on Applications of Computer Vision
(WACV) 2018, Lake Tahoe, US
PENGENALAN WAJAH MAHASISWA UNIVERSITAS NURUL JADID PADA VIDEO MENGGUNAKAN METODE HAAR CASCADE DAN DEEP LEARNING
Pengenalan wajah merupakan suatu teknologi dari komputer untuk mengidentifikasi wajah seseorang pada suatu gambar maupun video. Banyak metode yang bisa digunakan untuk pengenalan wajah antara lain metode fisherface, local binary pattern histogram, dan eigenface. Peneliti sebelumnya menerapkan pengenalan wajah menggunakan metode eigenface untuk mengidentifikasi wajah mahasiswa di Universitas Nurul Jadid. Akan tetapi, metode eigenface hanya fokus pada citra dengan objek tidak bergerak, sehingga belum bisa diterapkan pada video. Untuk itu, pada penelitian ini diusulkan suatu metode yang dapat mengidentifikasi wajah pada video yaitu metode haar cascade dan deep learning. Metode haar cascade merupakan suatu metode yang dapat mendeteksi posisi letak wajah pada suatu video dan metode deep learning untuk mengenali wajah yang sudah terdeteksi pada video. Hasil uji coba yang dilakukan metode haar cascade dapat mendeteksi adanya wajah pada video secara baik. Akan tetapi metode haar cascade juga mendeteksi yang bukan wajah pada data testing. Hasil dari uji coba pada gambar dengan metode haar cascade dan deep learning teridentifikasi secara benar dengan tingkat akurasi 99,6%. Hasil uji coba metode haar cascade dan deep learning pada video mahasiswa berhasil dilakukan jika komposisi warna dan tingkat cahayanya sama dengan data training dan jika tidak sesuai dengan data training maka tidak berhasil mengidentifikasi wajah mahasiswa pada video secara benar
DeepPose: Human Pose Estimation via Deep Neural Networks
We propose a method for human pose estimation based on Deep Neural Networks
(DNNs). The pose estimation is formulated as a DNN-based regression problem
towards body joints. We present a cascade of such DNN regressors which results
in high precision pose estimates. The approach has the advantage of reasoning
about pose in a holistic fashion and has a simple but yet powerful formulation
which capitalizes on recent advances in Deep Learning. We present a detailed
empirical analysis with state-of-art or better performance on four academic
benchmarks of diverse real-world images.Comment: IEEE Conference on Computer Vision and Pattern Recognition, 201
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
The design of complexity-aware cascaded detectors, combining features of very
different complexities, is considered. A new cascade design procedure is
introduced, by formulating cascade learning as the Lagrangian optimization of a
risk that accounts for both accuracy and complexity. A boosting algorithm,
denoted as complexity aware cascade training (CompACT), is then derived to
solve this optimization. CompACT cascades are shown to seek an optimal
trade-off between accuracy and complexity by pushing features of higher
complexity to the later cascade stages, where only a few difficult candidate
patches remain to be classified. This enables the use of features of vastly
different complexities in a single detector. In result, the feature pool can be
expanded to features previously impractical for cascade design, such as the
responses of a deep convolutional neural network (CNN). This is demonstrated
through the design of a pedestrian detector with a pool of features whose
complexities span orders of magnitude. The resulting cascade generalizes the
combination of a CNN with an object proposal mechanism: rather than a
pre-processing stage, CompACT cascades seamlessly integrate CNNs in their
stages. This enables state of the art performance on the Caltech and KITTI
datasets, at fairly fast speeds
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
Inspired by recent advances in deep learning, we propose a framework for
reconstructing MR images from undersampled data using a deep cascade of
convolutional neural networks to accelerate the data acquisition process. We
show that for Cartesian undersampling of 2D cardiac MR images, the proposed
method outperforms the state-of-the-art compressed sensing approaches, such as
dictionary learning-based MRI (DLMRI) reconstruction, in terms of
reconstruction error, perceptual quality and reconstruction speed for both
3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the
method proposed is approximately twice as small, allowing to preserve
anatomical structures more faithfully. Using our method, each image can be
reconstructed in 23 ms, which is fast enough to enable real-time applications
- …