- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Bioinspired Color Changing Molecular Sensor toward Early Fire Detection Based on Transformation of Phthalonitrile to Phthalocyanine
摘要： The fire detection plays a critical role in the maintenance of public security. Previous approaches of early fire warning, based on smoke or temperature response must be set in the proximity of a fire. They cannot provide the additional information of fire location or size and are susceptible to complicated situations. It is still a big challenge to make rapid and accurate early fire warning in precombustion because of the lack of reliable alarm signals. Herein, a precursor molecular sensor (PMS) is designed and synthesized that can present the chemical structure transformation to form phthalocyanines (Pcs) and release a color change signal at about 180 °C, learning from the plant chlorophyll metabolism. Further, the PMS is assembled to an early fire warning component (EWC) and an intelligent image recognition algorithm is introduced for unburned fire detection. The EWC generates a colorful alarm within 20 s at 275 °C. Therefore, the facile PMS provides a reliable real-time monitoring strategy to the early fire warning detection in precombustion.
关键词： color change,molecular sensor,image recognition algorithm,phthalocyanine,early fire detection
更新于2025-09-23 15:23:52
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Adaptive Weighted Multi-Task Sparse Representation Classification in SAR Image Recognition
摘要： In this paper, a novel multi-task sparse representation (MSR) of the monogenic signal is proposed in order to overcome the misclassification caused by heterogeneity of three components of the monogenic signal. In recent years, the monogenic signal has been applied into the field of SAR image recognition due to its capability of capturing the broad spectral information with maximal spatial localization. The monogenic signal can be decomposed into three components (local amplitude, local phase and local orientation) at different scales. The components are concatenated to three component-specific features and then fed into a MSR classification framework. However, the heterogeneity of the three component-specific features makes it difficult to make decisions by simply counting the accumulated error in multi-task sparse representation classification. To solve this problem, a multi-task learning model based on Fisher discrimination criteria is designed and Fisher score is presented to measure the discriminative ability of three types of component-specific feature in different classes. The final decision is made by weighted accumulated reconstruction error. Experiment results prove the effectiveness of adaptive weighted MSR classification method of monogenic signal.
关键词： multi-task sparse representation,image recognition,SAR
更新于2025-09-23 15:22:29
-
Application research of image recognition technology based on CNN in image location of environmental monitoring UAV
摘要： UAV remote sensing has been widely used in emergency rescue, disaster relief, environmental monitoring, urban planning, and so on. Image recognition and image location in environmental monitoring has become an academic hotspot in the field of computer vision. Convolution neural network model is the most commonly used image processing model. Compared with the traditional artificial neural network model, convolution neural network has more hidden layers. Its unique convolution and pooling operations have higher efficiency in image processing. It has incomparable advantages in image recognition and location and other forms of two-dimensional graphics tasks. As a new deformation of convolution neural network, residual neural network aims to make convolution layer learn a kind of residual instead of a direct learning goal. After analyzing the characteristics of CNN model for image feature representation and residual network, a residual network model is built. The UAV remote sensing system is selected as the platform to acquire image data, and the problem of image recognition based on residual neural network is studied, which is verified by experiment simulation and precision analysis. Finally, the problems and experiences in the process of learning and designing are discussed, and the future improvements in the field of image target location and recognition are prospected.
关键词： Residual network,CNN,Image recognition,UAV
更新于2025-09-23 15:22:29
-
[Advances in Intelligent Systems and Computing] Recent Findings in Intelligent Computing Techniques Volume 709 (Proceedings of the 5th ICACNI 2017, Volume 3) || Optimal Approach for Image Recognition Using Deep Convolutional Architecture
摘要： In the recent time, deep learning has achieved huge popularity due to its performance in various machine learning algorithms. Deep learning as hierarchical or structured learning attempts to model high-level abstractions in data by using a group of processing layers. The foundation of deep learning architectures is inspired by the understanding of information processing and neural responses in human brain. The architectures are created by stacking multiple linear or nonlinear operations. The article mainly focuses on the state-of-the-art deep learning models and various real-world application-speci?c training methods. Selecting optimal architecture for speci?c problem is a challenging task; at a closing stage of the article, we proposed optimal approach to deep convolutional architecture for the application of image recognition.
关键词： Deep neural networks,Image recognition,Image processing,Transfer learning,Convolutional neural networks,Deep learning
更新于2025-09-23 15:21:01
-
Human sensitivity to perturbations constrained by a model of the natural image manifold
摘要： Humans are remarkably well tuned to the statistical properties of natural images. However, quantitative characterization of processing within the domain of natural images has been difficult because most parametric manipulations of a natural image make that image appear less natural. We used generative adversarial networks (GANs) to constrain parametric manipulations to remain within an approximation of the manifold of natural images. In the first experiment, seven observers decided which one of two synthetic perturbed images matched a synthetic unperturbed comparison image. Observers were significantly more sensitive to perturbations that were constrained to an approximate manifold of natural images than they were to perturbations applied directly in pixel space. Trial-by-trial errors were consistent with the idea that these perturbations disrupt configural aspects of visual structure used in image segmentation. In a second experiment, five observers discriminated paths along the image manifold as recovered by the GAN. Observers were remarkably good at this task, confirming that observers are tuned to fairly detailed properties of an approximate manifold of natural images. We conclude that human tuning to natural images is more general than detecting deviations from natural appearance, and that humans have, to some extent, access to detailed interrelations between natural images.
关键词： natural images,image recognition,noise perturbations,artificial neural networks,generative adversarial nets
更新于2025-09-23 15:21:01
-
A New Local Knowledge-Based Collaborative Representation for Image Recognition
摘要： Recently, collaborative representation based classifiers (CRC) have shown outstanding performances in recognition tasks. The key to success of most CRC algorithms states that the testing samples can be coded well by a suitable dictionary globally, while the local knowledge between samples has not been fully considered. We observe that the representations of similar samples have a high degree of similarity. In order to take advantage of this important similarity information, this paper proposes a new local knowledge-based collaborative representation model for image classification. Specifically, certain adjacent training samples of the testing image should be determined firstly, and then the representations of these neighborhoods can be applied to guide the coefficients of the testing samples to be more discriminative. Further, we derive a robust version of the proposed method to treat the face recognition with occlusions or corruptions. Extensive experiments are carried out to show the superiority of the proposed method over other state-of-the-art classifiers on various image recognition tasks.
关键词： supervised learning,image recognition,robustness,Collaborative representation,local consistency
更新于2025-09-23 15:19:57
-
Image Recognition Based Automatic Decryption Method for Text Ecrypted Using Visual Cryptography
摘要： Using passwords only has rapidly become a security risk. Another approach to security is visual cryptography (VC), which divides paper documents into several encrypted papers managed by multiple people. Decryption occurs by stacking these papers, i.e., they cannot be decrypted individually. In our work, we consider a system for decrypting text encrypted by VC on digital devices. Furthermore, we propose a method for automatically recognizing encrypted portions using images captured by a digital device's camera. Our system has several advantages, including no actual text in communication and enabling users to use different passwords or secret questions at each use. Furthermore, our method is implementable on wearable glasses-like devices, thus enabling wearers to decrypt text simply by looking at encrypted portions. We conducted experiments regarding recognition accuracy and performance and obtained results showing that our proposed method was able to achieve a high recognition rate at a low cost.
关键词： Digital Device,Decryption,Visual Cryptography,Image Recognition
更新于2025-09-23 15:19:57
-
[Lecture Notes in Computer Science] Neural Information Processing Volume 11306 (25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part VI) || Fast Image Recognition with Gabor Filter and Pseudoinverse Learning AutoEncoders
摘要： Deep neural network has been successfully used in various ?elds, and it has received signi?cant results in some typical tasks, especially in computer vision. However, deep neural network are usually trained by using gradient descent based algorithm, which results in gradient vanishing and gradient explosion problems. And it requires expert level professional knowledge to design the structure of the deep neural network and ?nd the optimal hyper parameters for a given task. Consequently, training a deep neural network becomes a very time consuming problem. To overcome the shortcomings mentioned above, we present a model which combining Gabor ?lter and pseudoinverse learning autoencoders. The method referred in model optimization is a non-gradient descent algorithm. Besides, we presented the empirical formula to set the number of hidden neurons and the number of hidden layers in the entire training process. The experimental results show that our model is better than existing benchmark methods in speed, at same time it has the comparative recognition accuracy also.
关键词： Pseudoinverse learning autoencoder,Gabor ?lter,Handcraft feature,Image recognition
更新于2025-09-23 15:19:57
-
Research on path guidance of logistics transport vehicle based on image recognition and image processing in port area
摘要： Due to the messy logistics goods in the port area, some automatic transport vehicles often have errors in cargo transportation due to large path identification errors. Based on this, this study is based on image recognition technology, taking the most common logistics transport vehicles in the port area as the research object and using video image recognition technology as a guiding technology to perform image recognition processing on the ground guidance path. Simultaneously, this study determined the image preprocessing method which is more favorable for visual navigation, used the morphological knowledge of the image to detect the edge of the path image, then determined the position of the path center line, and carried out simulation analysis. The research shows that the results of this study have certain practicality and can provide theoretical references for subsequent related research.
关键词： Image recognition,Logistics transport vehicle,Image processing,Port area
更新于2025-09-19 17:15:36
-
New Evolutionary-Based Techniques for Image Registration
摘要： The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.
关键词： hybrid techniques,image recognition,image registration,firefly technique,evolutionary computing,affine perturbation,evolution strategies,mutual information
更新于2025-09-19 17:15:36