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We present the Region of Interest Autoencoder (ROIAE), a combined supervised and reconstruction model for the automatic visual detection of objects. More specifically, we augment the detection loss function with a reconstruction loss that targets only foreground examples. This allows us to exploit more effectively the information available in the sparsely populated foreground training data used in...
The transport of intensity equation (TIE) has established the relationship between the phase of an optical field and the variation of its intensity along the direction of propagation. In this paper, a new method based on TIE used for two-wavelength phase retrieval has been proposed. The TIE is solved by employing the regularized Fourier-transform-based-approach under two separate wavelengths, gaining...
Modern scientific instruments, such as detectors at synchrotron light sources, can generate data at 10s of GB/sec. Current experimental protocols typically process and validate data only after an experiment has completed, which can lead to undetected errors and prevents online steering. Real-time data analysis can enable both detection of, and recovery from, errors, and optimization of data acquisition...
Compressed sensing can represent the sparse signal with a small number of measurements compared to Nyquist-rate samples. Considering the high-complexity of reconstruction algorithms in CS, recently compressive detection is proposed, which performs detection directly in compressive domain without reconstruction. Different from existing work that generally considers the measurements corrupted by dense...
Compton Scatter Imaging is a promising approach in imaging science. Since it employs scattered radiation that is otherwise rejected, it is claimed to improve the sensitivity of current radiation-based imaging systems. The Radon transform on V-lines models a Compton Camera and its inversion through filtered back-projection leads to an efficient way to reconstruct the density of a radiotracer. Recently,...
Cone-beam CT (CBCT) imaging is heavily being utilized in radiotherapy treatment as means of treating cancer patients. One of the platforms is the Varian On-Board Imager (oBI) where kilo-volts (kV) CBCT imaging is used. In this study, a model of the OBI is developed to simulate the two scan modes that are available, namely: Full-Fan (FF) and Half-Fan (HF) modes. By shifting the same set of 1024×768...
We introduce a novel fast compressionless reconstruction algorithm for THz imaging. Based on Fourier imaging and a broadband THz spectrum, we demonstrate image reconstruction with high resolution using only 10s of pixels. First, we develop a mathematical framework based on polar Fourier optics and we demonstrate four cases where the fast reconstruction is possible. Second, we experimentally measure...
Terahertz technology is one of emerging technologies that has a potential to change our life. There is considerable interest in using electromagnetic terahertz pulses for imaging purposes. In this paper we presented initial results for improving the resolution of terahertz pulsed imaging system utilizing methodology of super-resolution reconstruction. We attempt to reconstruct an super-resolution(SR)...
We have evaluated the performance of the InP double heterojunction bipolar transistor and InP-based varactors as a room temperature sensitive detectors for THz computed tomography applications. They were used in transmission mode for the 350 GHz and 650 GHz atmospheric windows.
We report on the first steps towards THz imaging of biomechanical interfaces. A THz digital holography setup in reflection is used with synthetic aperture techniques to reconstruct the profile of a fingertip replica with a lateral resolution of 200 μm and a depth resolution of 20 μm. A method for the reconstruction of the profile of a sample hidden behind a THz-transparent material is also proposed...
Spectral imaging is useful in a wide range of applications for non-invasive detection and classification. However, the massive amount of involved data increases its processing and storing costs. In contrast, compressive spectral imaging (CSI) establishes that the three-dimensional data cube can be recovered from a small set of projections, that are generally captured in 2-dimensional detectors. Furthermore,...
This paper presents a novel method for detecting pedestrians under adverse illumination conditions. Our approach relies on a novel cross-modality learning framework and it is based on two main phases. First, given a multimodal dataset, a deep convolutional network is employed to learn a non-linear mapping, modeling the relations between RGB and thermal data. Then, the learned feature representations...
Matching local image descriptors is a key step in many computer vision applications. For more than a decade, hand-crafted descriptors such as SIFT have been used for this task. Recently, multiple new descriptors learned from data have been proposed and shown to improve on SIFT in terms of discriminative power. This paper is dedicated to an extensive experimental evaluation of learned local features...
Sparse representation has been successfully used to solve target detection problem in hyperspectral images (HSI). Compared with the traditional target detection methods, it is not fully dependent on statistical structure of the data sets. In this paper, a hybrid sparsity and constrained energy minimization (HSCEM) detector for HSI is proposed. In sparse representation, local clustering or unmixing...
This paper proposes a target detector based on kernel sparse and spatial constraint for hyperspectral imagery (HSI). Due to the nonlinear and structural features of HSI data, sparse representation and spatial constraint are taken into consideration. Firstly, we construct a dictionary to represent the target pixels within a small neighborhood by a linear combination of samples. Then, these targets...
A novel spectral-spatial joint multiscale approach is developed to address the multi-class change detection problem in bitemporal multispectral remote sensing images. The proposed approach is based on a multiscale morphological compressed change vector analysis (M2C2VA), which extend the state-of-the-art spectrum-based compressed change vector analysis (C2VA) while preserving more geometrical details...
A portable gamma ray tomography system has been designed to investigate geothermal power plant pipe scaling. Furthermore, the system could be applied to diagnose industrial processes unit such as pipeline, heat exchanger, and so on. The system consists of mechanical parts, computerized controlled module, a gamma ray source (80 mCi of Cs-137), a scintillation detector NaI(Tl), data acquisition and...
Spectral computed tomography (SCT) makes use of the spectral dependence of X-ray attenuation in tissues and contrast agents to separate the attenuation data into more than two energy bins. Current SCT detectors are costly and the measured data have low signal to noise ratio due to the detector's narrow bin bandwidth and quantum noise. A new approach called coded aperture compressive X-ray SCT that...
Single pixel detectors find applications ranging from multispectral imaging, through polarimetric, 3d, and holographic imaging, up to optical encryption and imaging through scattering media, which are however all hindered by the high measurement and reconstruction times. In this paper we propose to reduce the required signal acquisition time by using non-ergodic stationary sampling correlated with...
Lenslet images that record both spatial and angular light radiance in a super high definition with distinct macropixel structures desire efficient compression methods for promoting the applications of handheld plenoptic cameras urgently. In this paper, a lenslet image compression method is proposed. First, a reversible image reshaping and adaptive interpolation is proposed to align the macropixel...
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