Advanced Image and Video Processing Using MATLAB

Gong, Shengrong; Liu, Chunping; Jun, Yi Byung; Zhong, Baojiang; Li, Yonggang; Dong, Husheng

doi:10.1007/978-3-319-77223-3

Cited by 20 publications

(14 citation statements)

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“…The SUSAN algorithm uses a circular template to move over the image [36]. If the difference between the gray level of the pixel in the template and that of the central pixel is lower than the given threshold [37], then it is considered that the point has a similar gray level with the central pixel of the template [38].…”

Section: Methodsmentioning

confidence: 99%

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Sun

Zhang

2019

Wireless Communications and Mobile Computing

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Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Sun

Zhang

2019

Wireless Communications and Mobile Computing

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“…e image differencing technique [30] is adapted for foreground extraction. Gait cycle is defined as the time interval between successive instances of initial foot-to-floor contact by the same foot [31]. For gait cycle detection, we have considered two consecutive local minima of the bounding box as the start and endpoints of the gait cycle.…”

Section: Preprocessingmentioning

confidence: 99%

An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

Masood

Farooq

2021

International Journal of Optics

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Gait recognition-based person identification is an emerging trend in visual surveillance due to its uniqueness and adaptability to low-resolution video. Existing gait feature extraction techniques such as gait silhouette and Gait Energy Image rely on the human body’s shape. The shape of the human body varies according to the subject’s clothing and carrying conditions. The clothing choice changes every day and results in higher intraclass variance and lower interclass variance. Thus, gait verification and gait recognition are required for person identification. Moreover, clothing choices are highly influenced by the subject’s cultural background, and publicly available gait datasets lack the representation of South Asian Native clothing for gait recognition. We propose a Dynamic Gait Features extraction technique that preserves the spatiotemporal gait pattern with motion estimation. The Dynamic Gait Features under different Use Cases of clothing and carrying conditions are adaptable for gait verification and recognition. The Cross-Correlation score of Dynamic Gait Features resolves the problem of Gait verification. The standard deviation of Cross-Correlation Score lies in the range of 0.12 to 0.2 and reflects a strong correlation in Dynamic Gait Features of the same class. We achieved 98.5% accuracy on Support Vector Machine based gait recognition. Additionally, we develop a multiappearance-based gait dataset that captures the effects of South Asian Native Clothing (SACV-Gait dataset). We evaluated our work on CASIA-B, OUISIR-B, TUM-IITKGP, and SACV-Gait datasets and achieved an accuracy of 98%, 100%, 97.1%, and 98.8%, respectively.

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“…The deep neural network is used for solution and process the complex problems like recognition of images, natural language and computer vision for large data. In addition, it can classify images features automatically using Deep Learning [21,23,24,25]. Deep Learning mostly uses the CNN algorithm.…”

Section: Edge Detectionmentioning

confidence: 99%

“…The high dimensional filter is convolved with the inputs (images and videos) in this layer. CNN is useful for reducing the memory and the number of network parameters [25,26]. In this work, one of the most applications of CNN is to recognize the ASL letters for the hearing-impaired and deaf persons.…”

Section: Edge Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Hand Gesture Recognition of Static Letters American Sign Language (ASL) Using Deep Learning

Abdulhussein¹,

Raheem²

2020

ETJ

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An American Sign Language (ASL) is a complex language. It is depending on the special gesture stander of marks. These marks are represented by hands with assistance by facial expression and body posture. ASL is the main communication language of deaf and people who have hard hearing from North America and other parts of the world. In this paper, Gesture recognition is proposed of static ASL using Deep Learning. The contribution consists of two solutions to the problem. The first one is resized with Bicubic static ASL binary images. Besides that, good recognition results in of detection the boundary hand using the Robert edge detection method. The second solution is to classify the 24 alphabets static characters of ASL using Convolution Neural Network (CNN) and Deep Learning. The classification accuracy equals to 99.3 % and the error of loss function is 0.0002. According to 36 minutes with 15 seconds of elapsed time result and 100 iterations. The training is fast and gives the very good results, in comparison with other related works of CNN, SVM, and ANN for training.

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Advanced Image and Video Processing Using MATLAB

Cited by 20 publications

References 0 publications

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

Hand Gesture Recognition of Static Letters American Sign Language (ASL) Using Deep Learning

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