Framework for Content-Based Image Identification with Standardized Multiview Features

Das, Rik; Thepade, Sudeep D.; Ghosh, Swaroop

doi:10.4218/etrij.16.0115.0102

Cited by 13 publications

(6 citation statements)

References 40 publications

(39 reference statements)

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“…However, the shortcomings of CMOS are also obvious. Since each pixel is equipped with an amplifier and A/D conversion circuit [6], the photosensitive area of each pixel becomes smaller, so the sensitivity is lower than that of CCD. Moreover, due to the process, CMOS is more prone to noise during data transmission, resulting in signal distortion.…”

Section: A Ccdmentioning

confidence: 99%

Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

et al. 2020

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Forest vegetation is the main body that constitutes forest resources. Accurate identification of the types of forest trees can lay the foundation for the research and utilization of forest resources. With the development of remote sensing technology, traditional optical remote sensing can only describe the horizontal pattern of ground features, which makes it difficult to identify single tree species. Therefore, it is of great significance to study the method of forest tree image recognition. This paper mainly studies the forest image recognition system based on CCD and theodolite. In this paper, the forest image recognition system based on CCD and theodolite uses near, middle, and far CCD cameras to detect the infrared radiation of the target and collect the target image. The image processing algorithm is designed for the image processing module, and the flow chart of the image processing algorithm is given. The processing function has designed the interface of the image processing module. The image processing module can extract the main information of the target from simple background and complex background. In this paper, an experimental optical path is built, the forest image recognition simulation platform is verified, and the data obtained from the experiment is processed. The implemented color detection algorithm can achieve a detection accuracy rate of more than 91% for forest tree image recognition detection. The test results show that the image acquisition, transmission and display functions of the camera system realized by this subject are normal, and the system can achieve accurate recognition of the target.

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Section: A Ccdmentioning

confidence: 99%

Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

et al. 2020

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“…The ratio of inter-class variance to intra-class variance corresponds to the scattering matrix, which reflects the distribution of patterns in pattern space. Also the greater the similarity of the pixels of each class are the classification results will be better [12,13]. Therefore, the maximum inter-class variance criterion function S (t) is used to fine tune the rough threshold.…”

Section: Moving Video Image Processingmentioning

confidence: 99%

Fast recognition method of moving video images based on BP neural networks

Shao

Witarsyah

2018

Open Physics

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At present, the accuracy of real-time moving video image recognition methods are poor. Also energy consumption is high and fault tolerance is not ideal. Consequently this paper proposes a method of moving video image recognition based on BP neural networks. The moving video image is divided into two parts: the key content and the background by binary gray image. By collecting training cubes. The D-SFA algorithm is used to extract moving video image features and to construct feature representation. The image features are extracted by collecting training cubes. The BP neural network is constructed to get the error function. The error signal is returned continuously along the original path. By modifying the weights of neurons in each layer, the weights propagate to the input layer step by step, and then propagates forward. The two processes are repeated to minimize the error signal. The result of image feature extraction is regarded as the input of BP neural network, and the result of moving video image recognition is output. And fault tolerance in real-time is better than the current method. Also the recognition energy consumption is low, and our method is more practical.

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“…At that time, some scientific research institutions, academic libraries and urban public libraries began to research and explore the construction of digital libraries. In September 1993, the National Science Foundation, the National Aeronautics and Space Administration and the Advanced Research Center Program Agency of the United States Department of Defense jointly issued the "Digital Library Initiative", which kicked off the comprehensive construction of digital libraries in the United States [5].…”

Section: Introductionmentioning

confidence: 99%

Research on Aided Reading System of Digital Library Based on Text Image Features and Edge Computing

Shi

Zhu

2020

IEEE Access

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Framework for Content-Based Image Identification with Standardized Multiview Features

Cited by 13 publications

References 40 publications

Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

Fast recognition method of moving video images based on BP neural networks

Research on Aided Reading System of Digital Library Based on Text Image Features and Edge Computing

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