Fruit category identification is important in factories, supermarkets, and other fields. Current computer vision systems used handcrafted features, and did not get good results. In this study, our ...team designed a 13-layer convolutional neural network (CNN). Three types of data augmentation method was used: image rotation, Gamma correction, and noise injection. We also compared max pooling with average pooling. The stochastic gradient descent with momentum was used to train the CNN with minibatch size of 128. The overall accuracy of our method is 94.94%, at least 5 percentage points higher than state-of-the-art approaches. We validated this 13-layer is the optimal structure. The GPU can achieve a 177× acceleration on training data, and a 175× acceleration on test data. We observed using data augmentation can increase the overall accuracy. Our method is effective in image-based fruit classification.
Labeling brain images as healthy or pathological cases is an important procedure for medical diagnosis. Therefore, we proposed a novel image feature, stationary wavelet entropy (SWE), to extract ...brain image features. Meanwhile, we replaced the feature extraction procedure in state-of-the-art approaches with the proposed SWE. We found the classification performance improved after replacing wavelet entropy (WE), wavelet energy (WN), and discrete wavelet transform (DWT) with the proposed SWE. This proposed SWE is superior to WE, WN, and DWT.
Photoacoustic imaging, with the capability to provide simultaneous structural, functional, and molecular information, is one of the fastest growing biomedical imaging modalities of recent times. As a ...hybrid modality, it not only provides greater penetration depth than the purely optical imaging techniques, but also provides optical contrast of molecular components in the living tissue. Conventionally, photoacoustic imaging systems utilize bulky and expensive class IV lasers, which is one of the key factors hindering the clinical translation of this promising modality. Use of LEDs which are portable and affordable offers a unique opportunity to accelerate the clinical translation of photoacoustics. In this paper, we first review the development history of LED as an illumination source in biomedical photoacoustic imaging. Key developments in this area, from point-source measurements to development of high-power LED arrays, are briefly discussed. Finally, we thoroughly review multiple phantom, ex-vivo, animal in-vivo, human in-vivo, and clinical pilot studies and demonstrate the unprecedented preclinical and clinical potential of LED-based photoacoustic imaging.
Highlight removal is a fundamental and challenging task that has been an active field for decades. Although several methods have recently been improved for facial images, they are typically designed ...for a single image. This paper presents a lightweight optimization method for removing the specular highlight reflections of multi-view facial images. This is achieved by taking full advantage of the Lambertian consistency, which states that the diffuse component does not vary with the change in the viewing angle, while the specular component changes the behavior. We provide non-negative constraints on light and shading in all directions, rather than normal directions contained in the face, to obtain physically reliable properties. The removal of highlights is further facilitated through the estimation of illumination chromaticity, which is done by employing orthogonal subspace projection. An important practical feature of the proposed method does not require face reflectance priors. A dataset with ground truth for highlight removal of multi-view facial images is captured to quantitatively evaluate the performance of our method. We demonstrate the robustness and accuracy of our method through comparisons to existing methods for removing specular highlights and improvement in applications such as reconstruction.
Accurate motion capture plays an important role in sports analysis, the medical field and virtual reality. Current methods for motion capture often suffer from occlusions, which limits the accuracy ...of their pose estimation. In this paper, we propose a complete system to measure the pose parameters of the human body accurately. Different from previous monocular depth camera systems, we leverage two Kinect sensors to acquire more information about human movements, which ensures that we can still get an accurate estimation even when significant occlusion occurs. Because human motion is temporally constant, we adopt a learning analysis to mine the temporal information across the posture variations. Using this information, we estimate human pose parameters accurately, regardless of rapid movement. Our experimental results show that our system can perform an accurate pose estimation of the human body with the constraint of information from the temporal domain.
Cell-free massive multi-input-multi-output (MIMO) systems comprise a large number of distributed access points (APs) to serve a small number of user equipments (UEs). In this paper, ...compute-and-forward (CF) is investigated for uplink in cell-free massive MIMO. We propose a novel reverse CF precoding scheme for downlink transmission for cell-free massive MIMO. The APs send the linear equations of messages over a multiple access channel, and the UEs recover messages without interferences. The max–min power allocation is investigated to provide uniformly good service to all UEs. Numeral results demonstrate the performance improvement provided by the proposed reverse CF precoder against zero-forcing, conjugate beamforming, and linear minimum mean-square error precoder.
The morphology of dendritic spines is highly correlated with the neuron function. Therefore, it is of positive influence for the research of the dendritic spines. However, it is tried to manually ...label the spine types for statistical analysis. In this work, we proposed an approach based on the combination of wavelet contour analysis for the backbone detection, wavelet packet entropy, and fuzzy support vector machine for the spine classification. The experiments show that this approach is promising. The average detection accuracy of "MushRoom" achieves 97.3%, "Stubby" achieves 94.6%, and "Thin" achieves 97.2%.
FUS-proteinopathies, a group of heterogeneous disorders including ALS-FUS and FTLD-FUS, are characterized by the formation of inclusion bodies containing the nuclear protein FUS in the affected ...patients. However, the underlying molecular and cellular defects remain unclear. Here we provide evidence for mitochondrial localization of FUS and its induction of mitochondrial damage. Remarkably, FTLD-FUS brain samples show increased FUS expression and mitochondrial defects. Biochemical and genetic data demonstrate that FUS interacts with a mitochondrial chaperonin, HSP60, and that FUS translocation to mitochondria is, at least in part, mediated by HSP60. Down-regulating HSP60 reduces mitochondrially localized FUS and partially rescues mitochondrial defects and neurodegenerative phenotypes caused by FUS expression in transgenic flies. This is the first report of direct mitochondrial targeting by a nuclear protein associated with neurodegeneration, suggesting that mitochondrial impairment may represent a critical event in different forms of FUS-proteinopathies and a common pathological feature for both ALS-FUS and FTLD-FUS. Our study offers a potential explanation for the highly heterogeneous nature and complex genetic presentation of different forms of FUS-proteinopathies. Our data also suggest that mitochondrial damage may be a target in future development of diagnostic and therapeutic tools for FUS-proteinopathies, a group of devastating neurodegenerative diseases.
Cerebral micro-bleed (CMB) is small perivascular hemosiderin deposits from leakage through cerebral small vessels. They can result from cerebra-vascular disease, dementia, or simply from normal ...aging. It can be visualized via the susceptibility weighted imaging (SWI). Based on the SWI, we propose to use different structures of the CNN with rank-based average pooling to detect the CMB, and compare this method used in this paper to the current state-of-the-art methods. We can find that the CNN with five layers obtains the best performance, with a sensitivity of 96.94%, a specificity of 97.18%, and an accuracy of 97.18%.
Detecting obstacles in the rail track area is crucial for ensuring the safe operation of trains. However, this task presents numerous challenges, including the diverse nature of intrusions, and the ...complexity of the driving environment. This paper presents a multimodal fusion rail-obstacle detection approach by key points processing and rail track topology reconstruction. The core idea is to leverage the rich semantic information provided by images to design algorithms for reconstructing the topological structure of railway tracks. Additionally, it combines the effective geometric information provided by LiDAR to accurately locate the railway tracks in space and to filter out intrusions within the track area. Experimental results demonstrate that our method outperforms other approaches with a longer effective working distance and superior accuracy. Furthermore, our post-processing method exhibits robustness even under extreme weather conditions.
PDF
