Impact of compressed and down-scaled training images on vehicle detection in remote sensing imagery

Karim, Shahid; Zhang, Ye; Yin, Shoulin; Laghari, Asif Ali; Brohi, Ali Anwar

doi:10.1007/s11042-019-08033-x

Cited by 38 publications

(17 citation statements)

References 55 publications

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“…Preprocessing operations involve noise removal, smoothing, edge detection (18) (9) , slant correction and skew detection (19) , image normalization (20) (5) , thinning (21) (22) or keletonization, and baseline detection (23) (24) . The process of separating (25)(26)(27) and partitioning the images is known as segmentation (28) (29) . Segmentation is the still challenging and complex problem in the field of image processing.…”

Section: Online Image Acquisition 2 Offline Image Acquisitionmentioning

confidence: 99%

Multilingual OCR systems for the regional languages in Balochistan

Sanjrani¹

2020

IJST

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Background: There are various languages for which an optical character recognition technology has been developed but most of these address a particular language and thereby multilingual OCR remains a challenge. Methods: Development of multilingual OCR is one of a highly debated issue. Researcher are studying the feasibility and operational feasibility of multilingual OCR from technical as well as from viable aspects. Multilingual OCR includes printed or handwritten characters' form. In this paper, we study the significance, challenges and issues of developing multilingual OCR system for regional language based on Persio-Arabic script by conducting a comprehensive survery about the operational viability of mmultilingual OCR. Findings: A feedback of 339 participants is collected through an online surgery to find the scope and applicability of multilingual OCR. The respondents were from different linguistic background. The study identified that a large majority of participants are willing to use their native language for the accomplishment of their computational task and deemed that the support of multiple languages in a software would increase their productivity. Novelty: In current form, the study addresses the viability of multilingual OCR of regional language based on Persio-Arabic script. To the best of our knowledge, such kind of study has not been conducted for the domain of Pakistan.

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Section: Online Image Acquisition 2 Offline Image Acquisitionmentioning

confidence: 99%

Multilingual OCR systems for the regional languages in Balochistan

Sanjrani¹

2020

IJST

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“…Compressed and large-scale images are the problems in the dataset to accurately detect objects, this will help to easily detect vehicle and monitor their actives [96]. Poor visual and Low resolution of the satellite image is also an issue in object/vehicle detection, but development advance ML algorithms and model will help to use of Maximally Stable Extremal Regions for vehicle detection in complex situations low lightening conditions or under shadow regions [97,98].…”

Section: Open Research Areasmentioning

confidence: 99%

Machine Learning in Computer Vision: A Review

Khan

Laghari

Awan³

2018

ICST Transactions on Scalable Information Systems

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INTRODUCTION:Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human. The scenario of manual feeding of the machine is changed in the modern era, it will learn automatically. Supervised and unsupervised ML techniques are used as a distinct purpose like feature extraction, pattern recognition, object detection, and classification. OBJECTIVES:In Computer Vision (CV), ML performs a significant role to extract crucial information from images. CV successfully contributes to multiple domains, surveillance system, optical character recognition, robotics, suspect detection, and many more. The direction of CV research is going toward healthcare realm, medical imaging (MI) is the emerging technology, play a vital role to enhance image quality and recognized critical features of binary medical image, covert original image into grayscale and set the threshold values for segmentation.CONTRIBUTION: This paper will address the importance of machine learning, state-of-the-art, and how ML is utilized in computer vision and image processing. This survey will provide details about the type of tools and applications, datasets, and techniques. Limitations of previous work and challenges of future work also discussed. Further, we identify and discuss a set of open issues yet to be addressed, for efficiently applying of ML in Computer vision and image process. METHODS, RESULTS, AND CONCLUSION:In this review paper, we have discussed the techniques and various types of supervised and unsupervised algorithms of ML, general overview of image processing and the results based on the impact; neural network enabled models, limitations, tools and application of CV, moreover, highlight the critical open research areas of ML in CV.

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“…The channel attention mechanism can learn the importance of each feature channel, increase the channel features that are useful for current recognition according to the importance, and suppress the channel features with weak recognition power. This paper introduces the channel attention implementation network SE-net (Squeeze-andexcitation network) to the BN-inception [18]. The SE-BN-Inception module is obtained to calibrate the information of different channels and enhance the expression ability of video features.…”

Section: Se-bn-inception Modulementioning

confidence: 99%

A spatio-temporal attention fusion model for students behaviour recognition

Wang¹

2018

ICST Transactions on Scalable Information Systems

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Student behavior analysis can reflect students' learning situation in real time, which provides an important basis for optimizing classroom teaching strategies and improving teaching methods. It is an important task for smart classroom to explore how to use big data to detect and recognize students behavior. Traditional recognition methods have some defects, such as low efficiency, edge blur, time-consuming, etc. In this paper, we propose a new students behaviour recognition method based on spatio-temporal attention fusion model. It makes full use of key spatio-temporal information of video, the problem of spatio-temporal information redundancy is solved. Firstly, the channel attention mechanism is introduced into the spatio-temporal network, and the channel information is calibrated by modeling the dependency relationship between feature channels. It can improve the expression ability of features. Secondly, a time attention model based on convolutional neural network (CNN) is proposed, which uses fewer parameters to learn the attention score of each frame, focusing on the frames with obvious behaviour amplitude. Meanwhile, a multi-spatial attention model is presented to calculate the attention score of each position in each frame from different angles, extract several saliency areas of behaviour, and fuse the spatio-temporal features to further enhance the feature representation of video. Finally, the fused features are input into the classification network, and the behaviour recognition results are obtained by combining the two output streams according to different weights. Experiment results on HMDB51, UCF101 datasets and eight typical classroom behaviors of students show that the proposed method can effectively recognize the behaviours in videos. The accuracy of HMDB51 is higher than 90%, that of UCF101 and real data are higher than 90%.

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Impact of compressed and down-scaled training images on vehicle detection in remote sensing imagery

Cited by 38 publications

References 55 publications

Multilingual OCR systems for the regional languages in Balochistan

Multilingual OCR systems for the regional languages in Balochistan

Machine Learning in Computer Vision: A Review

A spatio-temporal attention fusion model for students behaviour recognition

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