111 research outputs found
U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires
many thousand annotated training samples. In this paper, we present a network
and training strategy that relies on the strong use of data augmentation to use
the available annotated samples more efficiently. The architecture consists of
a contracting path to capture context and a symmetric expanding path that
enables precise localization. We show that such a network can be trained
end-to-end from very few images and outperforms the prior best method (a
sliding-window convolutional network) on the ISBI challenge for segmentation of
neuronal structures in electron microscopic stacks. Using the same network
trained on transmitted light microscopy images (phase contrast and DIC) we won
the ISBI cell tracking challenge 2015 in these categories by a large margin.
Moreover, the network is fast. Segmentation of a 512x512 image takes less than
a second on a recent GPU. The full implementation (based on Caffe) and the
trained networks are available at
http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .Comment: conditionally accepted at MICCAI 201
A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery
We propose a neural network component, the regional aggregation layer, that
makes it possible to train a pixel-level density estimator using only
coarse-grained density aggregates, which reflect the number of objects in an
image region. Our approach is simple to use and does not require
domain-specific assumptions about the nature of the density function. We
evaluate our approach on several synthetic datasets. In addition, we use this
approach to learn to estimate high-resolution population and housing density
from satellite imagery. In all cases, we find that our approach results in
better density estimates than a commonly used baseline. We also show how our
housing density estimator can be used to classify buildings as residential or
non-residential.Comment: 10 pages, 8 figures. ACM SIGSPATIAL 2018, Seattle, US
Separating Reflection and Transmission Images in the Wild
The reflections caused by common semi-reflectors, such as glass windows, can
impact the performance of computer vision algorithms. State-of-the-art methods
can remove reflections on synthetic data and in controlled scenarios. However,
they are based on strong assumptions and do not generalize well to real-world
images. Contrary to a common misconception, real-world images are challenging
even when polarization information is used. We present a deep learning approach
to separate the reflected and the transmitted components of the recorded
irradiance, which explicitly uses the polarization properties of light. To
train it, we introduce an accurate synthetic data generation pipeline, which
simulates realistic reflections, including those generated by curved and
non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.Comment: accepted at ECCV 201
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network
Depth estimation from a single image is a fundamental problem in computer
vision. In this paper, we propose a simple yet effective convolutional spatial
propagation network (CSPN) to learn the affinity matrix for depth prediction.
Specifically, we adopt an efficient linear propagation model, where the
propagation is performed with a manner of recurrent convolutional operation,
and the affinity among neighboring pixels is learned through a deep
convolutional neural network (CNN). We apply the designed CSPN to two depth
estimation tasks given a single image: (1) To refine the depth output from
state-of-the-art (SOTA) existing methods; and (2) to convert sparse depth
samples to a dense depth map by embedding the depth samples within the
propagation procedure. The second task is inspired by the availability of
LIDARs that provides sparse but accurate depth measurements. We experimented
the proposed CSPN over two popular benchmarks for depth estimation, i.e. NYU v2
and KITTI, where we show that our proposed approach improves in not only
quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5
times faster) than prior SOTA methods.Comment: 14 pages, 8 figures, ECCV 201
Deep Learning for ECG Segmentation
We propose an algorithm for electrocardiogram (ECG) segmentation using a
UNet-like full-convolutional neural network. The algorithm receives an
arbitrary sampling rate ECG signal as an input, and gives a list of onsets and
offsets of P and T waves and QRS complexes as output. Our method of
segmentation differs from others in speed, a small number of parameters and a
good generalization: it is adaptive to different sampling rates and it is
generalized to various types of ECG monitors. The proposed approach is superior
to other state-of-the-art segmentation methods in terms of quality. In
particular, F1-measures for detection of onsets and offsets of P and T waves
and for QRS-complexes are at least 97.8%, 99.5%, and 99.9%, respectively.Comment: 10 pages, 7 figure
Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
Diabetic Retinopathy (DR) is a leading cause of blindness in working age
adults. DR lesions can be challenging to identify in fundus images, and
automatic DR detection systems can offer strong clinical value. Of the publicly
available labeled datasets for DR, the Indian Diabetic Retinopathy Image
Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of
four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard
exudates. We utilize the HEDNet edge detector to solve a semantic segmentation
task on this dataset, and then propose an end-to-end system for pixel-level
segmentation of DR lesions by incorporating HEDNet into a Conditional
Generative Adversarial Network (cGAN). We design a loss function that adds
adversarial loss to segmentation loss. Our experiments show that the addition
of the adversarial loss improves the lesion segmentation performance over the
baseline.Comment: Accepted to International Conference on Image Analysis and
Recognition, ICIAR 2019. Published at
https://doi.org/10.1007/978-3-030-27272-2_29 Code:
https://github.com/zoujx96/DR-segmentatio
ANALISA LAPORAN KEUANGAN SEBAGAI DASAR PENGUKURAN TINGKAT PROFITABILITAS PADA PERUSAHAAN DAERAH AIR MINUM (PDAM) KABUPATEN KARAWANG PERIODE 2002-2004
Perusahaan Daerah Air Minum (PDAM) Kabupaten Karawang adalah perusahaan yang bergerak dalam bidang jasa penyediaan air minum yang didistribusikan kepada masyarakat. Berdasarkan laporan keuangan PDAM Kabupaten Karawang yang diambil dalam penelitian pada periode 2002 hingga periode 2004 menggambarkan adanya fluktuasi pada salah satu rasio profitabilitasnya, yaitu pada rasio Basic Earning Power.
Adapun tujuan yang ingin dicapai dalam penelitian ini adalah untuk mengetahui kondisi keuangan perusahaan, mengetahui analisa laporan keuangan sebagai dasar pengukuran profitabilitas perusahaan serta mengetahui faktor-faktor yang mempengaruhi laporan keuangan terhadap profitabilitas perusahaan. Metode penelitian yang digunakan adalah metode penelitian deskriptif dan teknik pengumpulan data yang digunakan adalah studi kepustakaan dan studi lapangan yang terdiri dari wawancara terstruktur dan observasi non partisipan. Penelitian tersebut dilakukan dengan menggunakan metode analisis data horizontal, sedangkan teknik analisis data yang dipergunakan adalah dengan teknik analisis trend dan analisis rasio profitabilitas.
PDAM Kabupaten Karawang didalam perkembangan tingkat profitabilitasnya mengalami fluktuasi, terutama pada rasio basic earning powernya. Tingkat fluktuasi atas rasio profitabilitas perusahaan tersebut dipengaruhi oleh kenaikan tarif dasar air per m3 sebesar 40% hingga akhir periode 2004, selain itu didukung pula dengan peningkatan frekuensi sambungan rekening baru dengan mayoritas konsumen dari golongan niaga. Berdasarkan uraian diatas, maka hasil analisa laporan keuangan dapat dijadikan sebagai dasar pengukuran tingkat profitabilitas perusahaan.
Faktor-faktor yang mempengaruhi laporan keuangan terhadap tingkat profitabilitas pada PDAM Karawang yaitu tingginya biaya pengolahan air melebihi biaya pembelian air baku, lemahnya kinerja bagian Satuan Pengendalian Internal sehingga memudahkan terjadinya penyimpangan disipliner serta sering terjadinya keterlambatan penyampaian laporan keuangan cabang sehingga penyusunan laporan pusat seringkali tidak tetap dan cenderung kadaluarsa.
Saran yang peneliti kemukakan yaitu PDAM Karawang seharusnya menerapkan metode komputerisasi yang terkoneksi pada seluruh kantor cabang serta kerjasama dengan pihak luar (bank), guna memperkecil penyimpangan, mempermudah koordinasi serta memudahkan proses penyampaian data laporan keuangan setiap cabang sehingga diharapkan tidak akan terjadi lagi keterlambatan, yang kedua meningkatkan kinerja Satuan Pengendalian Internal agar mampu mengurangi bentuk penyimpangan
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