An effective local feature descriptor for object detection in real scenes

Nigam, Swati; Khare, Manish; Srivastava, Rajneesh; Khare, Ashish

doi:10.1109/cict.2013.6558098

Cited by 15 publications

(4 citation statements)

References 25 publications

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“…27 Local features and their descriptors constitute the basis for many computer vision algorithms. 28 Their applications include image registration, 29 object detection 30 and classification, 31 tracking, 32 motion estimation, 33 and content-based image retrieval. 34 Local features refer to a pattern or distinct structure that exists in an image, such as a point, edge, or small patch of the image.…”

Section: Image Registration Of Polarimetric Imagesmentioning

confidence: 99%

Registration of polarimetric images for in vivo skin diagnostics

et al. 2022

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. Significance: Mueller matrix (MM) polarimetry is a promising tool for the detection of skin cancer. Polarimetric in vivo measurements often suffer from misalignment of the polarimetric images due to motion, which can lead to false results. Aim: We aim to provide an easy-to-implement polarimetric image data registration method to ensure proper image alignment. Approach: A feature-based image registration is implemented for an MM polarimeter for phantom and in vivo human skin measurements. Results: We show that the keypoint-based registration of polarimetric images is necessary for in vivo skin polarimetry to ensure reliable results. Further, we deliver an efficient semiautomated method for the registration of polarimetric images. Conclusions: Image registration for in vivo polarimetry of human skin is required for improved diagnostics and can be efficiently enhanced with a keypoint-based approach.

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Section: Image Registration Of Polarimetric Imagesmentioning

confidence: 99%

Registration of polarimetric images for in vivo skin diagnostics

et al. 2022

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“…A key component of the HOG feature is how it is appropriate to have the local process of object and considering the indifference of object conversion and brightness state as edge and data‐based gradient has been evaluated under the applications of coordinate‐HOG feature vector. This can be formulated in the following Equations (Nigam et al, 2013):

G_{x} = N * I (x, y) and G_{x} = N^{T} * I (x, y)

…”

Section: Proposed Modelmentioning

confidence: 99%

An intelligent optimization algorithm with a deep learning‐enabled block‐based motion estimation model

Mishra

Kohli

2022

Expert Systems

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The motion estimation process has gained significant attention in several video applications such as video compression, global motion estimation, and so on. The deep learning (DL) models that perform effectively in computer vision tasks can be employed for motion estimation. Yet, they have high computational complexity and model parameters since motion estimation in the pixel domain is not effective in practical applications. Instead, a block-based motion estimation process has been developed where the blocks of sequential images are compared. To that end, this paper presents the Anas platyrhynchos optimizer with deep learning-enabled blockbased motion estimation (APODL-BBME) model. The proposed model estimates motion using block-based concepts and DL approaches. To accomplish this, the training and testing frames are separated into non-overlapping block categories, and the blocks are filtered via the bilateral filtering (BF) approach. In addition, a histogram of gradients (HOG) and densely connected network (DenseNet) model are employed to extract features, which are then fed into the bidirectional long short-term memory (BiLSTM) model to classify the input features. Finally, the APO algorithm is applied to optimally tune the hyperparameters of the BiLSTM model, which helps to improve the overall motion estimation efficacy, showing the novelty of the work. To demonstrate the enhanced performance of the APODL-BBME model, a comprehensive analysis is carried out, and when we compare the results, the APODL-BBME model outperforms recent motion estimation approaches.

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“…The classifier classifies vectors of histogram of oriented gradients (HOG) features that are extracted from 50 by 15 pixel images. HOG features are used because they [28]. Information on the method for extracting a HOG feature vector from an image can be found in [26].…”

Section: B Algorithmmentioning

confidence: 99%

“…HOG features are used because they are invariant to geometric transformations and illumination, and they can quickly be computed. For these reasons, HOG features are preferable to other types of features and have been widely used in computer vision for object detection since they were first described by Dalal and Triggs in 2005 [26]- [28]. Information on the method for extracting a HOG feature vector from an image can be found in [26].…”

Section: B Algorithmmentioning

confidence: 99%

Autonomous robotic system using non-destructive evaluation methods for bridge deck inspection

Gibb

Pham

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments.

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An effective local feature descriptor for object detection in real scenes

Cited by 15 publications

References 25 publications

Registration of polarimetric images for in vivo skin diagnostics

Registration of polarimetric images for in vivo skin diagnostics

An intelligent optimization algorithm with a deep learning‐enabled block‐based motion estimation model

Autonomous robotic system using non-destructive evaluation methods for bridge deck inspection

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