Significance of processing chrominance information for scene classification: a review

Sowmya, V.; Govind, D.; Soman, K. P.

doi:10.1007/s10462-018-09678-0

Cited by 4 publications

(8 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the quantitative relationship between OpenGL perspective imaging view functional parameters and internal and external orientation elements, the projection matrix and model view required for camera imaging of urban virtual 2 Wireless Communications and Mobile Computing geographic scene image are calculated through camera position, attitude, and internal parameters. According to the inverse process of OpenGL urban virtual geographic scene imaging, the imaging projection ray of the target in three-dimensional space is calculated, and the intersection of the ray and the three-dimensional scene is obtained, that is, the actual coordinates of the synthetic recognition target in the real world, where the calculation formula of pixel coordinates of the monitoring target imaging on the simulation image is as follows [14]:…”

Section: Research On the Methods Of Synchronousmentioning

confidence: 99%

Synchronized Information Acquisition Method for Virtual Geographic Scene Image Synthesis in Cities Based on Wireless Network Technology

Liu

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

At present, in the process of image synthesis information acquisition of urban virtual geographic scene, there is information complexity. The existing acquisition technology is easy to disturb in the process of information positioning and transmission, resulting in large acquisition delay and affecting the final synthesis quality of the image. In response to the above problems, the method of synchronous information acquisition for urban virtual geographic scene image synthesis based on wireless network technology is studied. Based on the construction of an urban virtual geographic environment, the spatial localization of the sign target of geographic scene image synthesis is carried out, and the optical flow method is used to register the urban geographic scene images. Based on the greedy algorithm of the beacon to design the synchronous wireless network route for urban virtual scene image synthesis, and after the grid division of the wireless network in the synthesis information acquisition area, the packets of each acquisition node are acquired and transmitted according to the designed wireless network route to realize the synchronous acquisition of image synthesis information. The comparison experimental data show that the acquisition delay of the studied information synchronization acquisition method is less than 0.5 s, the acquisition synchronization rate is significantly improved, and the quality of the synthesized images is better by applying the information acquired by the method, and the practical use is better.

show abstract

Section: Research On the Methods Of Synchronousmentioning

confidence: 99%

Synchronized Information Acquisition Method for Virtual Geographic Scene Image Synthesis in Cities Based on Wireless Network Technology

Liu

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Based on the bag-of-words (BoW) model introduced for text classification, a bag of visual words-based approach has been proposed, wherein different classifiers can be used in two stages for image classification 27 – 32 In the development of an image classification system using the BoW model, the first stage is the formation of unsupervised formation clusters, followed by histogram feature generation 33 – 35 The derived histogram features are then classified using supervised algorithms 24 , 36 , 37 …”

Section: Review On Image Classification Systemsmentioning

confidence: 99%

“…Although various machine-learning models used for the classification of images from multiple classes are mathematically well defined, there are dependencies on various parameters for feature-based classification. For instance, in a SIFT-GMM-based image classification, the system must be tuned for the parameters associated with feature extraction, followed by the tuning of the GMM parameters 20 , 38 , 39 . Techniques exist for estimating the optimal values of the parameters for the models.…”

Section: Review On Image Classification Systemsmentioning

confidence: 99%

“…Robustness used in this study refers to the generality of the class information represented by the models. A few studies have reported the issues of generality in discriminating various classes under limited data conditions using CNNs 20 , 38 , 45 – 47 The impact of aliasing in CNN-based acoustic models built from speech signals have been studied by Gong et al 48 .…”

Section: Review On Image Classification Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Automatic one-hand gesture (mudra) identification in bharatanatyam using eigenmudra projections and convolutional neural networks

2023

View full text Add to dashboard Cite

Mudras in traditional Indian dance forms convey meaningful information when performed by an artist. The subtle changes between the different mudras in a dance form render automatic identification challenging as compared to conventional hand gesture identification, where the gestures are uniquely distinct from each other. Therefore, the objective of this study is to build a classifier model for the identification of the asamyukta mudra of bharatanatyam, one of the most popular classical dance forms in India. The first part of the paper provides a comprehensive review of the issues present in bharatanatyam mudra identification and the various studies conducted on the automatic classification of mudras. Based on this review, we observe that the unavailability of a large mudra corpus is a major challenge in mudra identification. Therefore, the second part of the paper focuses on the development of a relatively large database of mudra images consisting of 29 asamyukta mudras prevalent in bharatanatyam, which is obtained by incorporating different variabilities, such as subject, artist type (amateur or professional), and orientation. The mudra image database so developed is made available for academic research purposes. The final part of this paper describes the development of a convolutional neural network (CNN)-based automatic mudra identification system. Multistyle training of mudra classes on a conventional CNN showed a 92% correct identification rate. Based on the "eigenface" projection used in face recognition, "eigenmudras" projections of mudra images are proposed for improving the CNN-based mudra identification. Although the CNNs trained on the eigenmudra-projected images provide nearly equal identification rates as that obtained using the CNNs trained on raw mudra grayscale images, both models provide complementary mudra class information. The presence of complementary class information is confirmed by the improvement in the mudra identification performance when the CNN models trained from the raw mudra and eigenmudra-projected images are combined by computing the average of the scores obtained in the final softmax layers of both models. The same trend of improved mudra identification is observed upon combination of the average score level of VGG19 CNN models of the raw mudra images and corresponding eigenmudra-projected images.

show abstract

“…The purpose of the scene recognition research is to use recognition algorithms to effectively process the semantic information contained in the image data, in order to extract the image features and determine the valid information of the category to which the scene image belongs. In the face of complex scene recognition problems, traditional scene recognition methods [10] gradually show limitations. Deep neural networks are able to learn the deep characteristics of images from a large number of sample images and show significant advantages in the field of image recognition, which better achieves low cost, high accuracy, and more stable navigation services [11].…”

Section: Introductionmentioning

confidence: 99%

Mobile Phone Indoor Scene Recognition Location Method Based on Semantic Constraint of Building Map

Liu¹,

Gao²,

Luo³

et al. 2022

Preprint

View full text Add to dashboard Cite

At present, indoor localization is one of the core technologies of location-based services (LBS), and there exist numerous scenario-oriented application solutions. Visual features, as the main semantic information to help people understand the environment and thus occupy the dominant part, many techniques about indoor scene recognition are widely adopted. However, the engineering application problem of cell phone indoor scene recognition and localization has not been well solved due to insufficient semantic constraint information of building map and the immaturity of building map location anchors (MLA) matching positioning technology. To address the above problems, this paper proposes a cell phone indoor scene recognition and localization method with building map semantic constraints. Firstly, we build a library of geocoded entities for building map location anchors (MLA), which can provide users with "immersive" real-world building maps on the one hand and semantic anchor point constraints for cell phone positioning on the other. Secondly, using the improved YOLOv5s deep learning model carried on the mobile terminal, we recognize the universal map location anchors (MLA) elements in building scenes by cell phone camera video in real-time. Lastly, the spatial location of the scene elements obtained from the cell phone video recognition is matched with the building MLA to achieve real-time positioning and navigation. The experimental results show that the model recognition accuracy of this method is above 97.2%, and the maximum localization error is within the range of 0.775 m, and minimized to 0.5 m after applying the BIMPN road network walking node constraint, which can effectively achieve high positioning accuracy in the building scenes with rich MLA element information. In addition, the building map location anchors (MLA) has universal characteristics, and the positioning algorithm based on scene element recognition is compatible with the extension of indoor map data types, so this method has good prospects for engineering applications.

show abstract

Significance of processing chrominance information for scene classification: a review

Cited by 4 publications

References 60 publications

Synchronized Information Acquisition Method for Virtual Geographic Scene Image Synthesis in Cities Based on Wireless Network Technology

Synchronized Information Acquisition Method for Virtual Geographic Scene Image Synthesis in Cities Based on Wireless Network Technology

Automatic one-hand gesture (mudra) identification in bharatanatyam using eigenmudra projections and convolutional neural networks

Mobile Phone Indoor Scene Recognition Location Method Based on Semantic Constraint of Building Map

Contact Info

Product

Resources

About