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Kernel density estimation is a popular method for identifying crime hotspots for the purpose of data-driven policing. However, computing a kernel density estimate is computationally intensive for large crime datasets, and the quality of the resulting estimate depends heavily on parameters that are difficult to set manually. Inspired by methods from image processing, we propose a novel way for performing...
We study the effectiveness of using convolutional neural networks (CNNs) to automatically detect abnormal heart and lung sounds and classify them into different classes in this paper. Heart and respiratory diseases have been affecting humankind for a long time. An effective and automatic diagnostic method is highly attractive since it can help discover potential threat at the early stage, even at...
Vessel segmentation of digital retinal images plays an important role in diagnosis of diseases such as diabetics, hypertension and retinopathy of prematurity due to these diseases impact the retina. In this paper, a novel Size-Invariant Fully Convolutional Neural Network (SIFCN) is proposed to address the automatic retinal vessel segmentation problems. The input data of the network is the patches...
In ultrasound image analysis, speckle tracking methods are widely applied to study the elasticity of body tissue. However, “feature-motion decorrelation” still remains as a challenge for speckle tracking methods. Recently, a coupled filtering method was proposed to accurately estimate strain values when the tissue deformation is large. The major drawback of the new method is its high computational...
Even though face recognition in frontal view and normal lighting condition works very well, the performance degenerates sharply in extreme conditions. In real applications, both the lighting and pose variation will always be encountered at the same time. Accordingly we propose an end-to-end face recognition method to deal with pose and illumination simultaneously based on convolutional neural networks...
The fractional Fourier transform has been proved useful both in theory and in applications of signal processing as well as optics. We present a single channel sampling theorem for signals in shift-invariant spaces associated with the fractional Fourier transform domain.
Subjectivity detection aims to distinguish natural language as either opinionated (positive or negative) or neutral. In word vector based convolutional neural network models, a word meaning is simply a signal that helps to classify larger entities such as a document. Previous works do not usually consider prior distribution when using sliding windows to learn word embedding's and, hence, they are...
Target detection is a hard real-time task for video and image processing. This task has recently been accomplished through the feedforward process of convolutional neural net-works (CNN), which is usually accelerated by general-purpose graphic units (GPUs). However, there is a challenge for this task. The running speed remains to be improved. In this paper, we present an efficient image combination...
Convolution operations dominate the total execution time of deep convolutional neural networks (CNNs). In this paper, we aim at enhancing the performance of the state-of-the-art convolution algorithm (called Winograd convolution) on the GPU. Our work is based on two observations: (1) CNNs often have abundant zero weights and (2) the performance benefit of Winograd convolution is limited mainly due...
Area integral invariant (AII) is a functional obtained by performing integral operations on the closed planar contour of a shape via the convolution with disc kernels. This shape descriptor is insensitive to noise and robust with respect to occlusions. AII intrinsically introduces the notion of scale using the size of kernel radius. However how to select an optimal scale remains unresolved. In this...
Magnetic resonance imaging (MRI) plays an important role in early diagnosis, which can accurately capture the disease variations of the anatomical brain structure. We propose a novel method for improving feature extraction performance from magnetic resonance images (MRI). This study presents a combination of multi-channel input and 3D convolutional neural network architecture which can reduce the...
Multi-scale Retinex algorithm is an image enhancement algorithm that aims at image reconstruction. The algorithm maintains the high fidelity and the dynamic range compression of the image, so the enhancement effect is obvious. The algorithm exploits a large number of convolution operations to achieve dynamic range compression and color/brightness rendition, and the calculation time increased significantly...
Sliding window convolutional networks (ConvNets) have become a popular approach to computer vision problems such as image segmentation and object detection and localization. Here we consider the parallelization of inference, i.e., the application of a previously trained ConvNet, with emphasis on 3D images. Our goal is to maximize throughput, defined as the number of output voxels computed per unit...
Analysis of near-infrared images has a possibility to simply find vein disease. If super-resolution (SR) techniques improve the quality of near-infrared images with a low signal-to-noise ratio, they could detect abnormal veins at an early stage. Deep convolutional neural networks (DCNNs) as a SR technique were applied to downgraded images, and the effectiveness was investigated. The DCNNs with the...
The details of oriented visual stimuli are better resolved when they are horizontal or vertical rather than oblique. This "oblique effect" has been researched and confirmed in numerous research studies, including behavioral studies and neurophysiological and neuroimaging findings. Although the "oblique effect" has influence in many fields, little research integrated it into computational...
This study presents a new method based on convolutional neural network (CNN) for the gearbox fault identification and classification, which does not need the complex feature extraction process as those traditional recognition algorithms do, and it also depress the uncertainty of arbitrary feature selection. The vibration signals of the gearbox under normal and hybrid fault conditions were collected,...
Parabolic motion cameras are used to obtain better deblurring results of scenes with multiple moving objects. The core of its deblurring process is Iterative Re-weighted Least Squares (IRLS) method. In this paper, we design a hardware accelerator for IRLS flow. The ASIC chip is implemented using TSMC 90 nm technology. It is capable of deblurring a 640 × 480 image captured by a parabolic camera with...
In this paper, an object detection and localization method for bin picking of plastic wrapped objects is described. Since such objects are deformable and have non-Lambertian surfaces, it is difficult to apply conventional feature point approaches or edge based template matching. To solve this problem, we propose a new method which is called “KCS (kernel convolution score)”. It measures the total score...
Deep Convolutional Neural Network (CNN) is one of the most popular methods for image processing and recognition. There are many research works to improve the performance of CNNs. However, as an important part of CNNs, convolution kernel has rarely been discussed. As one Original Convolution Kernel (OCK) can only detect one type of visual feature with a fixed deformation, the networks using OCKs may...
The popularity of neural networks (NNs) spans academia [1], industry [2], and popular culture [3]. In particular, convolutional neural networks (CNNs) have been applied to many image based machine learning tasks and have yielded strong results [4]. The availability of hardware/software systems for efficient training and deployment of large and/or deep CNN models is critical for the continued success...
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