Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed

Unsupervised Domain Adaptation for Multispectral Pedestrian Detection

Multimodal information (e.g., visible and thermal) can generate robust pedestrian detections to facilitate around-the-clock computer vision applications, such as autonomous driving and video surveillance. However, it still remains a crucial challenge to train a reliable detector working well in different multispectral pedestrian datasets without manual annotations. In this paper, we propose a novel unsupervised domain adaptation framework for multispectral pedestrian detection, by iteratively generating pseudo annotations and updating the parameters of our designed multispectral pedestrian detector on target domain. Pseudo annotations are generated using the detector trained on source domain, and then updated by fixing the parameters of detector and minimizing the cross entropy loss without back-propagation. Training labels are generated using the pseudo annotations by considering the characteristics of similarity and complementarity between well-aligned visible and infrared image pairs. The parameters of detector are updated using the generated labels by minimizing our defined multi-detection loss function with back-propagation. The optimal parameters of detector can be obtained after iteratively updating the pseudo annotations and parameters. Experimental results show that our proposed unsupervised multimodal domain adaptation method achieves significantly higher detection performance than the approach without domain adaptation, and is competitive with the supervised multispectral pedestrian detectors

Weak Signal Detection Based on Adaptive Cascaded Bistable Stochastic Resonance System

AbstractStochastic resonance system is an effective method to extract weak signal, however, system output is directly influenced by system parameters. Aiming to this, a method about weak periodic signal extraction was developed based on adaptive stochastic resonance. Firstly cascaded stochastic resonance system was established in order to achieve better low-pass filtering effect. And then, variance of zero point distance was chosen as measurement index of cascade system. It's able to overcome the shortage that traditional adaptive stochastic resonance system needs to know the signal frequency beforehand. Also, it could obtain optimum system parameters adaptively. Basing on these parameters, input signal will be handled, and optimum output could be obtained. Furthermore, different periodic signal have been recognized, and finally the validity of the method is verified through simulation experiments

Study on Resource Configuration on Cloud Manufacturing

The purpose of manufacturing is to realize the requirement of customer. In manufacturing process of cloud system, there exist a lot of resource services which have similar functional characteristics to realize the requirement. It makes the manufacturing process more diverse. To develop the quality and reduce cost, a resource configuration model on cloud-manufacturing platform is put forward in this paper. According to the generalized six-point location principle, a growth design from the requirement of customers to entities with geometric constraints is proposed. By the requirement growing up to product, a configuration process is used to match the entities with the instances which the resources in the database could supply. Different from most existing studies, this paper studies the tolerance design with multiple candidate resource suppliers on cloud manufacturing to make the market play a two-level game considering the benefit of customers and the profit of resources to give an optimal result. A numerical case study is used to illustrate the proposed model and configuration process. The performance and advantage of the proposed method are discussed at the end

Learning Inter- and Intra-frame Representations for Non-Lambertian Photometric Stereo

In this paper, we build a two-stage Convolutional Neural Network (CNN) architecture to construct inter- and intra-frame representations based on an arbitrary number of images captured under different light directions, performing accurate normal estimation of non-Lambertian objects. We experimentally investigate numerous network design alternatives for identifying the optimal scheme to deploy inter-frame and intra-frame feature extraction modules for the photometric stereo problem. Moreover, we propose to utilize the easily obtained object mask for eliminating adverse interference from invalid background regions in intra-frame spatial convolutions, thus effectively improve the accuracy of normal estimation for surfaces made of dark materials or with cast shadows. Experimental results demonstrate that proposed masked two-stage photometric stereo CNN model (MT-PS-CNN) performs favorably against state-of-the-art photometric stereo techniques in terms of both accuracy and efficiency. In addition, the proposed method is capable of predicting accurate and rich surface normal details for non-Lambertian objects of complex geometry and performs stably given inputs captured in both sparse and dense lighting distributions.Comment: 9 pages,8 figure

NCP activates chloroplast transcription by controlling phytochrome-dependent dual nuclear and plastidial switches.

Phytochromes initiate chloroplast biogenesis by activating genes encoding the photosynthetic apparatus, including photosynthesis-associated plastid-encoded genes (PhAPGs). PhAPGs are transcribed by a bacterial-type RNA polymerase (PEP), but how phytochromes in the nucleus activate chloroplast gene expression remains enigmatic. We report here a forward genetic screen in Arabidopsis that identified NUCLEAR CONTROL OF PEP ACTIVITY (NCP) as a necessary component of phytochrome signaling for PhAPG activation. NCP is dual-targeted to plastids and the nucleus. While nuclear NCP mediates the degradation of two repressors of chloroplast biogenesis, PIF1 and PIF3, NCP in plastids promotes the assembly of the PEP complex for PhAPG transcription. NCP and its paralog RCB are non-catalytic thioredoxin-like proteins that diverged in seed plants to adopt nonredundant functions in phytochrome signaling. These results support a model in which phytochromes control PhAPG expression through light-dependent double nuclear and plastidial switches that are linked by evolutionarily conserved and dual-localized regulatory proteins

On-chip tuning of the resonant wavelength in a high-Q microresonator integrated with a microheater

We report on fabrication of a microtoroid resonator of high-quality (high-Q) factor integrated with an on-chip microheater. Both the microresonator and microheater are fabricated using femtosecond laser three-dimensional (3D) micromachining. The microheater, which is located about 200 micron away from the microresonator, has a footprint size of 200 micron by 400 micron. Tuning of the resonant wavelength in the microresonator has been achieved by varying the voltage applied on the microheater. The drifting of the resonant wavelength shows a linear dependence on the square of the voltage applied on the microheater. We found that the response time of the microresonator is less than 10 secs which is significantly shorter than the time required for reaching a thermal equilibrium on conventional heating instruments such as an external electric heater