Improved object recognition results using SIFT and ORB feature detector

Gupta, Surbhi; Kumar, Munish; Garg, Anupam

doi:10.1007/s11042-019-08232-6

Cited by 62 publications

(17 citation statements)

References 27 publications

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“…Using classifiers to train different terrain features, and then matched with the collected environment image information to achieve the terrain environment recognition. 26,27 In this study, random forest classifier is investigated for classification. 28 Random forest is a classifier that uses multiple trees to train and predict samples.…”

Section: Classificationmentioning

confidence: 99%

“…For comparison, k-NN, DT, SVM, and RF classifiers are used to classify the feature set in this research. 26 For obtaining classifier, the entire dataset containing 1000 samples are partitioned into a training dataset and a testing dataset. Three hundred images are captured under the natural condition, other 700 images are acquired from the MITplace2 image set.…”

Section: Simulation Experimentsmentioning

confidence: 99%

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Autonomous recognition technology of carrier robot on various terrain environment

Dong

Song²,

Luan³

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Field carrier robot can help people carry a certain amount of necessary materials in mountaineering, exploration and other fields, and even help field soldiers carry a certain amount of armaments. However, due to the complexity of the field environment, it is difficult for a robot with a single locomotion mechanism to complete such work. A carrier robot with leg/caterpillar/wheel is proposed. The robot can recognize different terrain environment, so as to select the appropriate locomotion mechanism, and can avoid obstacles and plan the path at the same time. We optimize the traditional ORB algorithm to extract the features of the environment image. The random forest classifier is used to classify the terrain, so as to start the corresponding locomotion mechanism. In addition, the extracted ORB feature points are also used for stereo matching to achieve the depth recovery for environmental information. Based on the analysis of two path planning algorithms of DWA and artificial potential field, an artificial potential field algorithm based on DWA is proposed for robot local path planning. In order to verify the effectiveness of these methods, experiments are carried out on the switching for three locomotion mechanisms and path planning in different practical environments. The results show that when the terrain changes, the robot can effectively switch the locomotion mechanism, and can run stably in different environments.

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Section: Classificationmentioning

confidence: 99%

Section: Simulation Experimentsmentioning

confidence: 99%

Autonomous recognition technology of carrier robot on various terrain environment

Dong

Song²,

Luan³

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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show abstract

“…Random Forest, K-Nearest Neighbours) have been also widely used in computer vision and modelling systems, i.e. Garg et al (2018), Kumar et al (2018), Gupta et al (2019a), Chhabra et al (2020), Bansal et al (2021a), Kumar et al (2021), Gupta et al (2021) and Bansal et al (2021b). In terms of resource planning in healthcare, a wide range of tools and techniques have been proposed including agent-based simulation (Cabrera et al 2012), discrete event simulation (Izady and Worthington 2012;Rossetti et al 1999;Ahmed and Alkhamis 2009), queuing theory (Belciug and Gorunescu 2015;Hou et al 2019), operating room scheduling (Adan et al 2009;Akbarzadeh et al 2019;Vandenberghe et al 2019) and ambulance deployment (Bertsimas and Ng 2019;Talarico et al 2015;Majzoubi et al 2012) among others.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A hybrid analytical model for an entire hospital resource optimisation

2021

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Given the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future.

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“…SIFT features are implemented along with ORB features to improve the object recognition task (2) . Bag of visual words technique is used for recognizing objects which build a visual vocabulary of extracted local features using SIFT/ ORB/ SURF feature descriptor algorithm etc.…”

Section: Introductionmentioning

confidence: 99%

“…Bag of visual words technique is used for recognizing objects which build a visual vocabulary of extracted local features using SIFT/ ORB/ SURF feature descriptor algorithm etc. However, the bag of visual vocabulary strategy as in (2) is complex and time-consuming. FER (Facial Expression Recognition) algorithm is integrated with online course platforms (3) due to the rapid development of online education.…”

Section: Introductionmentioning

confidence: 99%

Patch-SIFT: Enhanced feature descriptor to learn human facial emotions using an Ensemble approach

Hema¹,

Kannan²

2021

IJST

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Background: Having experienced more than a year of pandemic, a variety of applications such as online classrooms, virtual office meetings, conferences, online games, Social media & Networks, Mobile applications, and many other infotainment areas have made humans live with gadgets and respond to them. However, all these applications have an impact on human behavioral transformation. It is very significant for employers to understand the emotions of their employees in the era of online office & work from home concept to increase productivity. Learning and identifying emotions from the human face has its application in all online portals when physical contact could not be achieved. Ojbective: Human Facial emotions can be learned using enormous feature descriptors that extract image features. While local feature descriptors retrieve pixel-level information, global feature descriptors extract the overall image information. Both of the feature descriptors quantify the image information, however, they don't provide complete and relevant information. Hence, this research work aims to improve the existing local feature descriptor to perform globally for emotion recognition. Method: Our proposed feature descriptor, Patch-SIFT collects features from multiple patches within an image. This strategy is evolved to globally apply the local feature descriptor as a hybridization paradigm. The extracted features are trained and tested on an ensemble model. Findings: The Proposed Feature descriptor (Patch-SIFT) performance with ensemble model is found to produce an improved accuracy of 98% compared with existing feature descriptors and Machine learning classifiers. Novelty: This research work tries to evolve a new Feature descriptor algorithm based on SIFT algorithm for an efficient emotion recognition system that works without the need for any additional GPU or huge dataset.

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Improved object recognition results using SIFT and ORB feature detector

Cited by 62 publications

References 27 publications

Autonomous recognition technology of carrier robot on various terrain environment

Autonomous recognition technology of carrier robot on various terrain environment

A hybrid analytical model for an entire hospital resource optimisation

Patch-SIFT: Enhanced feature descriptor to learn human facial emotions using an Ensemble approach

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