Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor

Bozorgi, Hamed; Jafari, Ali

doi:10.1631/fitee.1500295

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Sift 33 : the proposed V‐FER model employs the sift method for extracting the face features from these input frames

{F}_i^{VJ}

. For the feature extraction, the potential feature points are considered based on the input frames

{F}_i^{VJ}

.…”

Section: Optimal Feature Selection Of Face Expression Dataset With Dy...mentioning

confidence: 99%

Dynamic FERNet: Deep learning with optimal feature selection for face expression recognition in video

Jagadeesh

Baranidharan

2022

Concurrency and Computation

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This work concentrates on the recognition of facial emotion from video sequences with deep learning. Once the input video is converted into frames, the face detection is performed on each frame using the viola-jones face detection algorithm.Then, the feature extraction is performed by three well-performing feature extraction techniques like modified local directional pattern, spatio-temporal features, and scale-invariant feature transforms. The extracted features from all the frames of the video are concatenated. To reduce the feature-length for decreasing the training complexity, and enhance the recognition performance, the optimal feature selection is accomplished with the distance-based tunicate swarm algorithm. These selected features are processed to an innovative deep learning model termed a heuristically modified recurrent neural network. The same D-TSA improves the performance of RNN by optimally tuning its hidden neurons. Experimental results on a widely used benchmark dataset and manually collected dataset show that the classification performance is improved using spatio-temporal features, SIFT, M-LDP, and optimal feature selection, and thus, the proposed model with HM-RNN outperforms the other existing models. K E Y W O R D Sdistance-based tunicate swarm algorithm, face expression recognition in the video, heuristically modified recurrent neural network, Viola-Jones face detection algorithm INTRODUCTIONEmotions in human beings are understood by their facial expressions that are analyzed to identify the nature of that individual and their intentions. 1Hence, understanding these facial expressions is a persistent issue in the area of human-computer interaction (HCI). 2 Thus, these facial analyzes are broadly researched based on computer vision, in which certain applications like facial expression recognition and face recognition seek more attention in the improvement of network architectures. 3 Face expression recognition provides automated analysis in the practical applications for recognizing the facial expressions in videos, which influences the interaction interface between the human robots, which is the study of making communications among the humans and robots. Recently, the facial recognition approaches analyze the video frames using six basic expressions and also evaluated the neutral expression in the frames. 4 Similarly, face recognition methods are considered to be a significant biometric technique in many fields for providing authentication. 5 These methods use dynamic features like variations in facial texture and facial landmarks that provide eminent information for showing the emotional status. Thus, extracting the dynamic information from the whole video sequence is essential for recognizing the facial expression. These facial expression analysis systems are employed for categorizing the image or video sequence among the six basic expressions into single basic emotion. 6

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“…Sift 33 : the proposed V‐FER model employs the sift method for extracting the face features from these input frames

{F}_i^{VJ}

. For the feature extraction, the potential feature points are considered based on the input frames

{F}_i^{VJ}

.…”

Section: Optimal Feature Selection Of Face Expression Dataset With Dy...mentioning

confidence: 99%

Dynamic FERNet: Deep learning with optimal feature selection for face expression recognition in video

Jagadeesh

Baranidharan

2022

Concurrency and Computation

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“…For improving the SIR outcomes, a suitable hybrid combination of descriptors are undertaken for this experimental analysis. After employing hybrid feature descriptors, the common point sets among source and referenced satellite pictures are determined [13,14]. Then, a suitable transform scheme is applied for transforming the source satellite pictures to be aligned with respect to reference satellite pictures.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

Anil*,

D.N

2019

IJITEE

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Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.

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An Improved Image Registration Method Using E-SIFT Feature Descriptor with Hybrid Optimization Algorithm

Swathi

Srinivas

2019

J Indian Soc Remote Sens

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Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor

Cited by 5 publications

References 21 publications

Dynamic FERNet: Deep learning with optimal feature selection for face expression recognition in video

Dynamic FERNet: Deep learning with optimal feature selection for face expression recognition in video

Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

An Improved Image Registration Method Using E-SIFT Feature Descriptor with Hybrid Optimization Algorithm

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