21 papers · ranked by Valyu relevance
Fisher Yu, Vladlen Koltun
State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction problems such as semantic segmentation are structurally different from image classification. In this work, we develop a new…
Cong Xu, Xuqi Wang, Shanwen Zhang
Accurate and rapid identification of apple leaf diseases is the basis for preventing and treating apple diseases. However, it is challenging to identify apple leaf diseases due to their various symptoms, different colors, irregular shapes, uneven sizes, and complex backgrounds. To reduce computational cost and improve…
Md. Aminur Rab Ratul, M. Hamed Mozaffari, Won-Sook Lee, Enea Parimbelli
The prediction of skin lesions is a challenging task even for experienced dermatologists due to a little contrast between surrounding skin and lesions, the visual resemblance between skin lesions, fuddled lesion border, etc. An automated computer-aided detection system with given images can help clinicians to prognosis…
Seyha Chim, Jin-Gu Lee, Ho-Hyun Park
Facial landmark detection has gained enormous interest for face-related applications due to its success in facial analysis tasks such as facial recognition, cartoon generation, face tracking and facial expression analysis. Many studies have been proposed and implemented to deal with the challenging problems of…
Tao Wang, Zenghui Ding, Xianjun Yang, Yanyan Chen + 4 more
'Xiaoming Kong' 'Yining Sun' 'Ivan Miguel Pires'] Mild cognitive impairment (MCI) is a precursor to neurodegenerative diseases such as Alzheimer’s disease, and an early diagnosis and intervention can delay its progression. However, the brain MRI images of MCI patients have small changes and blurry shapes. At the same…
Zhengyang Wang, Shuiwang Ji
—Dilated convolutions, also known as atrous convolutions, have been widely explored in deep convolutional neural networks (DCNNs) for various dense prediction tasks. However, dilated convolutions suffer from the gridding artifacts, which hampers the performance. In this work, we propose two simple yet effective…
Wei Wang, Yiyang Hu, Ting Zou, Hongmei Liu + 2 more
Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of…
Xiajiong Shen, Kunying Meng, Lei Zhang, Xianyu Zuo
The neural network method can obtain a higher precision of radar echo extrapolation than the traditional method. However, its application in radar echo extrapolation is still in the initial stage of exploration, and there is still much room for improvement in the extrapolation accuracy. To improve the utilization of…
Zhen Wang, Buhong Wang, Jianxin Guo, Shanwen Zhang
Underwater sonar objective detection plays an important role in the field of ocean exploration. In order to solve the problem of sonar objective detection under the complex environment, a sonar objective detection method is proposed based on dilated separable densely connected convolutional neural networks (DS-CNNs)…
Fengdan Hu, Haigen Hu, Hui Xu, Jinshan Xu + 2 more
Owing to the variable shapes, large size difference, uneven grayscale, and dense distribution among biological cells in an image, it is very difficult to accurately detect and segment cells. Especially, it is a serious challenge for some microscope imaging devices with limited resources owing to a large number of…
Thomas R. Ziegler, Manuel Fritsche, Lorenz Kuhn, Konstantin Donhauser
'Konstantin Donhauser'] Abstract— Dilated Convolutions have been shown to be highly useful for the task of image segmentation. By introducing gaps into convolutional filters, they enable the use of larger receptive fields without increasing the original kernel size. Even though this allows for the inexpensive capturing…
Gavneet Singh Chadha, Jan Reimann, Andreas Schwung
Vanilla convolutional neural networks are known to provide superior performance not only in image recognition tasks but also in natural language processing and time series analysis. One of the strengths of convolutional layers is the ability to learn features about spatial relations in the input domain using various…
Md. Aminur Rab Ratul, Mohammad Hamed Mozaffari, Enea Parimbelli, WonSook Lee
Skin cancer is a crucial public health issue and by far the most usual kind of cancer specifically in the region of North America. It is estimated that in 2019, only because of melanoma nearly 7,230 people will die, and 192,310 cases of malignant melanoma will be diagnosed. Nonetheless, nearly all types of skin lesions…
Yunhee Jeong, Muhammad Febrian Rachmadi, Maria del C. Valdés-Hernández, Taku Komura
White matter hyperintensities(WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image(MRI) sequences. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for Alzheimer’ s disease and brain aging processes. However, the automatic…
Samuel Olowofila, Oluwatosin Oluwadare
The spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains the leading method for unraveling 3D genome structures, but the limited availability of high-resolution Hi-C data poses significant challenges for comprehensive analysis. Deep…
Domen Tabernik, Matej Kristan, Aleš Leonardis
Classical deep convolutional networks increase receptive field size by either gradual resolution reduction or application of hand-crafted dilated convolutions to prevent increase in the number of parameters. In this paper we propose a novel displaced aggregation unit (DAU) that does not require hand-crafting. In…
Narendra Chaudhary, Sanchit Misra, Dhiraj Kalamkar, Alexander Heinecke + 4 more
Identifying accessible chromatin regions is a fundamental problem in epigenomics with ATAC-seq being a commonly used assay. Exponential rise in single cell ATAC-seq experiments has made it critical to accelerate processing of ATAC-seq data. ATAC-seq data can have a low signal-to-noise ratio for various reasons…
Authors not listed
Obtaining quantitative information about residence time behavior (i.e., the residence time distribution function) in realistic experimental systems is oftentimes experimentally challenging and numerically complex. The conventional way is to conduct very simple pulse or step tracer experiments or construct elaborate…
Jiahui Huang, Kshitij Dwivedi, Gemma Roig
Convolutional Neural Networks (CNNs) have been proven to be extremely successful at solving computer vision tasks. State-of-the-art methods favor such deep network architectures for its accuracy performance, with the cost of having massive number of parameters and high weights redundancy. Previous works have studied…
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…