Object Detection Using Scale Invariant Feature Transform

Nguyen, Thao; Park, Eun-Ae; Han, Jianmin; Park, Dong-Chul; Min, Soo-Young

doi:10.1007/978-3-319-01796-9_7

Cited by 30 publications

(14 citation statements)

References 9 publications

(15 reference statements)

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“…When the keypoint descriptors are received, they are often used as a feature or keypoint for data to solve various problems. More detailed information on SIFT computation are often found in [110], [111]. Kravets et al [90] proposed P-SIFT (Parallel SIFT) algorithm, which reduces the computation time and increases the processing speed.…”

Section: ) Scale Invariant Feature Transform (Sift)mentioning

confidence: 99%

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

et al. 2020

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With the advancements in machine and deep learning algorithms, the envision of various critical real-life applications in computer vision becomes possible. One of the applications is facial sentiment analysis. Deep learning has made facial expression recognition the most trending research fields in computer vision area. Recently, deep learning-based FER models have suffered from various technological issues like under-fitting or over-fitting. It is due to either insufficient training and expression data. Motivated from the above facts, this paper presents a systematic and comprehensive survey on current state-of-art Artificial Intelligence techniques (datasets and algorithms) that provide a solution to the aforementioned issues. It also presents a taxonomy of existing facial sentiment analysis strategies in brief. Then, this paper reviews the existing novel machine and deep learning networks proposed by researchers that are specifically designed for facial expression recognition based on static images and present their merits and demerits and summarized their approach. Finally, this paper also presents the open issues and research challenges for the design of a robust facial expression recognition system. INDEX TERMS Facial sentiment analysis, machine learning, deep learning, convolutional neural network, deep belief network, artificial intelligence.

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Section: ) Scale Invariant Feature Transform (Sift)mentioning

confidence: 99%

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

et al. 2020

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“…Histogram of oriented gradient-namely, HOG feature-is calculated and counted through the histogram of gradient direction of local area of the image, and finally constitutes features, which can effectively extract facial emotional features [7]. HOG feature and scale-invariant feature transform (SIFT) [41] are both calculated on a dense image grid with uniform interval, and overlapped local contrast normalization is used to improve performance. At present, HOG is mainly combined with an SVM (Support Vector Machine) classifier, which is mainly used for image recognition, and improves the performance in pedestrian detection.…”

Section: Hog Featuresmentioning

confidence: 99%

HOG-ESRs Face Emotion Recognition Algorithm Based on HOG Feature and ESRs Method

Zhong

Sun

et al. 2021

Symmetry

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As we all know, there are many ways to express emotions. Among them, facial emotion recognition, which is widely used in human–computer interaction, psychoanalysis of mental patients, multimedia retrieval, and other fields, is still a challenging task. At present, although convolutional neural network has achieved great success in face emotion recognition algorithms, it has a rising space in effective feature extraction and recognition accuracy. According to a large number of literature studies, histogram of oriented gradient (HOG) can effectively extract face features, and ensemble methods can effectively improve the accuracy and robustness of the algorithm. Therefore, this paper proposes a new algorithm, HOG-ESRs, which improves the traditional ensemble methods to the ensembles with shared representations (ESRs) method, effectively reducing the residual generalization error, and then combining HOG features with ESRs. The experimental results on the FER2013 dataset show that the new algorithm can not only effectively extract features and reduce the residual generalization error, but also improve the accuracy and robustness of the algorithm, the purpose of the study being achieved. The application of HOG-ESRs in facial emotion recognition is helpful to solve the symmetry of edge detection and the deficiency of related methods in an outdoor lighting environment.

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“…Those techniques are available from traditional object detection to the most refined implementation of pedestrian detection. Early methods in pedestrian detection are mainly focused on feature representation, such as SIFT [12], SURF [13]- [14], shape contexts [15], and the integral channel features (ICF) detector [16], which requires registration of the object to be searched for features in the images. These methods provide unlabeled data that the algorithm tries to understand by extracting its features and patterns so that they can only be used for certain, known objects.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Human Detection Using Deep Learning on Embedded Platforms: A Review

Rahmaniar

Hernawan

2021

jrc

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The detection of an object such as a human is very important for image understanding in the field of computer vision. Human detection in images can provide essential information for a wide variety of applications in intelligent systems. In this paper, human detection is carried out using deep learning that has developed rapidly and achieved extraordinary success in various object detection implementations. Recently, several embedded systems have emerged as powerful computing boards to provide high processing capabilities using the graphics processing unit (GPU). This paper aims to provide a comprehensive survey of the latest achievements in this field by using deep learning techniques in the embedded platforms. NVIDIA Jetson was chosen as a low power system designed to accelerate deep learning applications. This review highlights the performance of human detection models such as PedNet, multiped, SSD MobileNet V1, SSD MobileNet V2, and SSD inception V2 on edge computing. This survey provides an overview of these methods and compares their performance in accuracy and computation time for real-time applications. The experimental results show that the SSD MobileNet V2 model provides the highest accuracy with the fastest computation time compared to other models in our video datasets with several scenarios.

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Object Detection Using Scale Invariant Feature Transform

Cited by 30 publications

References 9 publications

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

HOG-ESRs Face Emotion Recognition Algorithm Based on HOG Feature and ESRs Method

Real-Time Human Detection Using Deep Learning on Embedded Platforms: A Review

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