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Here, the feature occlusion, a data augmentation method that simulates real‐life challenges on the main features of the human face for age and gender recognition is proposed. Previous methods achieved promising results on constrained data sets with strict environmental settings, but the results on unconstrained data sets are still far from perfect. The proposed method adopted three simple occlusion techniques, blackout, random brightness, and blur, and each simulates a different kind of challenge that would be encountered in real‐world applications. A modified cross‐entropy loss that gives less penalty to the age predictions that land on the adjacent classes of the ground truth class is also proposed. The effectiveness of our proposed method is verified by implementing the augmentation method and modified cross‐entropy loss on two different convolution neural networks, the slightly modified AdienceNet and the slightly modified VGG16, to perform age and gender classification. The proposed augmentation system improves the age and gender classification accuracy of the slightly modified AdienceNet network by 6.62% and 6.53% on the Adience data set, respectively. The proposed augmentation system also improves the age and gender classification accuracy of the slightly modified VGG16 network by 6.20% and 6.31% on the Adience data set, respectively.

This study describes the design and implementation of a 256-bit very long instruction word (VLIW) microprocessor based on the new RISC-V instruction set architecture (ISA). Base integer RV32I and extension instruction sets, including RV32M, RV32F, and RV32D, are selected to implement our VLIW hardware. The proposed architecture packs up eight 32-bit instruction flows, each of which performs fixed operational functions to create a 256-bit long instruction format. However, one obstacle of studying new ISAs, similar to RISC-V, to design VLIW microprocessors is the lack of dedicated compilers. Developing an architecture-specific compiler is really challenging. An instruction scheduler is integrated to dynamically schedule independent instructions into the VLIW instruction format. This scheduler is used to overcome the lack of a dedicated RISC-V VLIW compiler and leverage the available RISC-V GNU toolchain. Unlike conventional VLIWs, our proposed architecture is organized into six main stages, namely, fetch, instruction scheduler, decode, execute, data memory, and writeback. The complete design is verified, synthesized, and implemented on a Xilinx Virtex-6 (xc6vlx240t-1-ff1156). Maximum synthesis frequency reaches 83.739 MHz. The proposed RISC-V-based VLIW architecture obtains an average instructions per cycle value that outperforms that of existing open-source RISC-V cores.

As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years. However, in order to apply the deep learning model to edge devices accompanying sensors on vehicles, we need to design a structure that has the best trade-off between accuracy and inference time. In previous works, several methods sacrificed accuracy to obtain a faster inference time, while others aimed to find the best accuracy under the condition of real time. Nevertheless, the accuracies of previous real-time semantic segmentation methods still have a large gap compared to general semantic segmentation methods. As a result, we propose a network architecture based on a dual encoder and a self-attention mechanism. Compared with preceding works, we achieved a 78.6% mIoU with a speed of 39.4 FPS with a 1024 × 2048 resolution on a Cityscapes test submission.

This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images (restoration of rich details in hazy image). We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor. NH-Haze is the first realistic nonhomogeneous haze dataset that provides ground truth images. The nonhomogeneous haze has been produced using a professional haze generator that imitates the real conditions of haze scenes. 168 participants registered in the challenge and 27 teams competed in the final testing phase. The proposed solutions gauge the state-of-the-art in image dehazing., CVPR Workshops Proceedings 2020

Background The regulation of the balance between inflammatory and anti-inflammatory events during the treatment of pulmonary infection is very important. Soluble Schistosoma egg antigens (SEA) can effectively inhibit the expression of cytokines during hepatic acute inflammation. However, the mechanisms by which these proteins suppress the inflammatory responses in lung cells remain unclear. The purpose of this study was to investigate the ability of SEA to inhibit pulmonary inflammation. Methods The effects of SEA were investigated in LPS-treated lung IMR-90 cells. The involvement of the JAK/STAT-1 signaling pathway in these effects was evaluated by employing CBA assays, quantitative polymerase chain reaction, and western blotting experiments. Results Pretreatment of IMR-90 cells with appropriate concentrations of SEA protected cells against the cytotoxic effects of LPS-induced inflammation in a time-dependent manner. SEA pretreatment significantly attenuated the LPS-induced activation of the JAK/STAT1 signaling pathway, including the upregulation of JAK1/2 and STAT1, as well as the production of inflammatory cytokines. The level of phosphorylated STAT1 gradually declined in response to increasing concentrations of SEA. Based on these findings, we hypothesize that SEA-induced anti-inflammatory effects initiate with the downregulation of the IFN-γ-JAK-STAT1 signaling pathway, resulting in the attenuation of LPS-induced inflammation in IMR-90 cells. Conclusion Our study is the first to demonstrate the anti-inflammatory activity of SEA in an in vitro model of pulmonary inflammation, involving the modulation of JAK/STAT1 signaling. We propose SEA as potential therapeutic or preventive agents for the selective suppression of STAT1 and the control of inflammatory response in lung IMR-90 cells.

In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate.

High demand 3-D scenes on embedded systems draw the developers’ attention to use the whole resources of current low-power processors and add dedicated hardware as a graphic accelerator unit to deal with real-time realistic scene rendering. Photon mapping, as one of the most powerful techniques to render highly realistic 3-D images by high amounts of floating-point operations, is very time-consuming. To use the advantages of multiprocessor systems to make 3-D scenes, parallel photon-mapping rendering on a homogeneous multiprocessor SoC (MPSoC) platform along with a mesh NoC by an adaptive wormhole routing method to communicate packets among cores is proposed in this paper. To make efficient use of the MPSoC platform to carry out photon-mapping rendering, many methods concerning the increase of load balancing, the efficient use of memory, and the decrease of communication cost to achieve a scalable application are explored in this paper. The resulting MPSoC platform is verified and evaluated by cycle-accurate simulations for different sizes of the mesh NoC. As expected, the proposed methods can obtain excellent load balancing and achieve a maximum of 44.3 times faster on an 8-by-8 MPSoC platform than on a single-core MPSoC platform.

For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

A new method for detecting text in video images is proposed in this article. Variations in background complexity, font size and color, make detecting text regions in video images a difficult task. A pyramidal scheme is utilized to solve these problems. First, two downsized images are generated by bilinear interpolation from the original image. Then, the gradient difference of each pixel is calculated for three differently sized images, including the original one. Next, three K-means clustering procedures are applied to separate all the pixels of the three gradient difference images into two clusters: text and non-text, separately. The K-means clustering results are then combined to form the text regions. Thereafter, projection profile analysis is applied to the Sobel edge map of each text region to determine the boundaries of candidate text regions. Finally, we identify text candidates through two verification phases. In the first verification phase, we verify the geometrical properties and texture of each text candidate. In the second verification phase, statistical characteristics of the text candidate are computed using a discrete wavelet transform, and then the principal component analysis is further used to reduce the number of dimensions of these features. Next, the optimal decision function of the support vector machine, obtained by sequential minimal optimization, is applied to determine whether the text candidates contain texts or not.

In this article, we describe a distributed, peer-to-peer gesture recognition system along with a software architecture modeling technique and authority control protocol for ubiquitous cameras. This system performs gesture recognition in real time by combining imagery from multiple cameras without using a central server. We propose a system architecture that uses a network of inexpensive cameras to perform in-network video processing. A methodology for transforming well-designed single-node algorithm to distributed system is also proposed. Applications for ubiquitous cameras can be modeled as the composition of a finite-state machine of the system, functional services, and middleware. A service-oriented software architecture is proposed to dynamically reconfigure services when system state changes. By exchanging data and control messages between neighboring sensors, each node can maintain broader view of the environment with integrated video-processing results. Our prototype system is built on Windows machines, and uses standard video cameras as sensors and local network as a communication channel.

The investigations of emotional speech identification can be divided into two main parts, features and classifiers. In this paper, how to extract an effective speech feature set for the emotional speech identification is addressed. In our speech feature set, we use not only statistical analysis of frame-based acoustical features, but also the approximated speech feature contours, which are obtained by extracting extremely low frequency components to speech feature contours. Furthermore, principal component analysis (PCA) is applied to the approximated speech feature contours so that an efficient representation of approximated contours can be derived. The proposed speech feature set is fed into support vector machines (SVMs) to perform multiclass emotion identification. The experimental results demonstrate the performance of the proposed system with 82.26% identification rate.

This paper addresses issues pertaining to the necessity of utilizing formal verification methods in the design of protocols for peer-to-peer multimedia systems. These systems require sophisticated communication protocols, and these protocols require verification. We discuss two sample protocols designed for two distinct peer-to-peer computer vision applications, namely multi-object multi-camera tracking and distributed gesture recognition. We present simulation and verification results for these protocols, obtained by using the SPIN verification tool, and discuss the importance of verifying the protocols used in peer-to-peer multimedia systems.

In this reported work, the lowest level channel prior is proposed for image fog removal. The use of the lowest level channel is simplified from the dark channel prior. It is based on a key observation that fog-free intensity in a colour image is usually the minimum value of trichromatic channels. To estimate the transmission model, the dark channel prior then performs as a min filter for the lowest intensity. However, the min filter results in halo artefacts, specifically for neighbours of edge pixels. Instead of the min filter, this work utilises the exact O (1) bilateral filter to solve this problem. Experimental results show the high performance of the proposed method.