Improving automated latent fingerprint detection and segmentation using deep convolutional neural network

Chhabra, Megha; Ravulakollu, Kiran Kumar; Kumar, Manoj; Sharma, Abhay; Nayyar, Anand

doi:10.1007/s00521-022-07894-y

Cited by 35 publications

(13 citation statements)

References 58 publications

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“…In this process, the dataset is partitioned into 10 random folds, and each fold is representing a miniature version of the overall dataset, every time the training is done on the 9 samples and evaluation is done upon the rest samples, it changes iteratively. When the extracted features are capable of discriminating shaky and non-shaky videos, motivated by the conventional machine learning algorithms which do not require large number of samples for successful classification, we propose to use Random Forest classifier for classification in this work [19][20][21][22][23][24]. The Random Forest Classifier is a well-known technique for classification, which can handle imbalanced features and noise features and it can avoid overfitting problems.…”

Section: Proposed Methodsmentioning

confidence: 99%

Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Asadzadehkaljahi

Halder

Shivakumara

et al. 2023

AIA

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Arbitrary moving object detection including vehicles and human beings in the real environment, such as protected and sensitive areas, is challenging due to arbitrary deformation and directions caused by Shakey camera and wind. This work aims at adopting a Spatio-temporal approach for classifying arbitrarily moving objects. The proposed method segments foreground objects from the background using the frame difference between the median frame and individual frames. This step outputs several different foreground information. The mean of foreground images is computed, which is referred to as the mean activation map. For the mean activation map, the method employs the Fast Fourier Transform (FFT), which outputs amplitude and frequencies. The mean of frequencies is computed for moving objects in using activation maps of temporal frames, which is considered as a frequency feature vector. The features are normalized to avoid the problems of imbalanced features and class sizes. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples and the random forest classifier is used for the final classification of arbitrary moving and static videos. For evaluating the proposed method, we construct our dataset, which contains videos of static and arbitrarily moving objects caused by shaky cameras and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance.

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Section: Proposed Methodsmentioning

confidence: 99%

Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Asadzadehkaljahi

Halder

Shivakumara

et al. 2023

AIA

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“…For classification, the proposed work uses 10-fold cross-validation which provides the number of training and testing samples automatically. Since our goal is to develop simple and effective model which can fit for realtime environment, the proposed works adapt conventional machine learning models (Chhabra et al, 2022;Chithaluru et al, 2022;Kaushik et al, 2022) as classifier rather than deep learning models. For the final classification, the proposed work uses random forest (RF) classifier, which is a well-known technique for classification (Bharadwaj et al, 2022;Solanki et al, 2021).…”

Section: Proposed Methodsmentioning

confidence: 99%

Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes

Asadzadehkaljahi

Halder

Pal

et al. 2023

AIA

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Classification of arbitrary moving objects including vehicles and human beings in a real environment (such as protected and sensitive areas) is challenging due to arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatiotemporal approach for classifying arbitrarily moving objects. The intuition to propose the approach is that the behavior of the arbitrary moving objects caused by wind and shaky camera is inconsistent and unstable, while, for static objects, the behavior is consistent and stable. The proposed method segments foreground objects from background using the frame difference between median frame and individual frame. This step outputs several different foreground information. The method finds static and dynamic edges by subtracting Canny of foreground information from the Canny edges of respective input frames. The ratio of the number of static and dynamic edges of each frame is considered as features. The features are normalized to avoid the problems of imbalanced feature size and irrelevant features. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples, and the random forest classifier is used for the final classification of frames with static objects and arbitrarily moving objects. For evaluating the proposed method, we construct our own dataset, which contains video of static and arbitrarily moving objects caused by shaky camera and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance (76% classification rate) which is 14% better than the best existing method.

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“…Motivated by the superior performance of deep learning technology (Chhabra et al, 2022 ; Motwani et al, 2022 ; Shailendra et al, 2022 ; Singh et al, 2022 ), it has been applied in grasping detection to improve the accuracy of grasping in recent years. In order to improve the generalization of 3D models, some grab detection methods based on 3D reconstruction are proposed.…”

Section: Related Studiesmentioning

confidence: 99%

A neural learning approach for simultaneous object detection and grasp detection in cluttered scenes

Yang

Xie²,

Li³

2023

Front. Comput. Neurosci.

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Object detection and grasp detection are essential for unmanned systems working in cluttered real-world environments. Detecting grasp configurations for each object in the scene would enable reasoning manipulations. However, finding the relationships between objects and grasp configurations is still a challenging problem. To achieve this, we propose a novel neural learning approach, namely SOGD, to predict a best grasp configuration for each detected objects from an RGB-D image. The cluttered background is first filtered out via a 3D-plane-based approach. Then two separate branches are designed to detect objects and grasp candidates, respectively. The relationship between object proposals and grasp candidates are learned by an additional alignment module. A series of experiments are conducted on two public datasets (Cornell Grasp Dataset and Jacquard Dataset) and the results demonstrate the superior performance of our SOGD against SOTA methods in predicting reasonable grasp configurations “from a cluttered scene.”

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Improving automated latent fingerprint detection and segmentation using deep convolutional neural network

Cited by 35 publications

References 58 publications

Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes

A neural learning approach for simultaneous object detection and grasp detection in cluttered scenes

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