[PDF] Top 20 Firearm Recognition Using Convolutional Neural Network

Firearm Recognition Using Convolutional Neural Network

... During training, the only pre-processing step is to get the mean value of RGB, which is on the computed training data. After that the images are moved to the stack of convolutional layers. Three fully connected ... See full document

6

Candle Stick Pattern Recognition Using Convolutional Neural Network (ResNet)

... to neural networks have given great ...these neural networks model has laid path to the success pattern ...such Neural Networks. A practical pattern recognition system will be always composed ... See full document

7

Fruit Recognition Using Deep Convolutional Neural Network With Color Feature

... Abstract: Deep learning is a major part in the family machine learning. Learning is based on data representations and not like the traditional method which uses task-specific algorithms. There are different forms of deep ... See full document

5

Integrated Animal Recognition and Detection Using Deep Convolutional Neural Network

... Deep Convolutional Neural Network (DCNN) for the classification of the input animal images is ...proposals using multilevel graph cut in the spatiotemporal ...model using self-learned ... See full document

7

Human Face Recognition in Video using Convolutional Neural Network (CNN)

... pattern recognition and computer vision because of its application on various field such as RFID (Radio Frequency Identification) cards, smart cards, surveillance systems, pay systems and access ...face ... See full document

7

Design And Development Of A License Plate Recognition System Using Convolutional Neural Network

... face recognition [10-14], gender recognition [15, 16], object recognition [17-19], character recognition [20-22], texture recognition [23], ... See full document

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Handwritten Bangla Character Recognition using Inception Convolutional Neural Network

... of convolutional layer. On its own, a convolutional layer is not rotation invariant that means the model is not good at classifying rotating testing image ... See full document

12

Optimization of Convolutional Neural Network Target Recognition Algorithm

... optimized convolutional neural network target recognition algorithm for the problem of low recognition rate of synthetic aperture radar (SAR) target training, under the condition of ... See full document

8

Android-Based Rice Variety Classifier (Arvac) Using Convolutional Neural Network

... image recognition system using Convolutional Neural Network was used as a new method to classify and identify rice variety in terms of visual features such as size, color, shape, and ... See full document

5

Three-dimensional convolutional restricted Boltzmann machine for human behavior recognition from RGB-D video

... weight-sharing network structure with the biological neural networks makes it possible to reduce the complexity of network model as well as the number of ...the network is a multi-dimen- ... See full document

11

Robust Face Recognition Based on Convolutional Neural Network

... Maxout network that the maximum feature map activation function (MFM) is derived in order to replace the close representation of the ReLU sparse ...the network to better global ... See full document

6

Vehicle Model Recognition Based on Convolutional Neural Network

... model recognition method and explores the impacts of parameter setting, number of convolutional layers and moving average model on the recognition accuracy through ...the recognition accuracy ... See full document

6

Handwritten Digit Recognition: Convolutional Neural Network as a Classifier

... layers Neural networks in general and CNNs in particular rely on a non-linear “trigger” function to signal distinct identificationof likely features on each hidden ...overall network without affecting the ... See full document

6

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

... Exploring the effectiveness of the combinations of cost‑sensitive and data‑level methods Since the data-level methods and cost-sensitive are two powerful techniques for address- ing the imbalanced dataset from different ... See full document

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A NOVEL TWO DIMENSIONAL SPECTRAL/SPATIAL HYBRID CODE FOR OPTICAL CODE DIVISION MULTIPLE ACCESS SYSTEM

... An ear recognition method is proposed by Revaud et al. [18], which uses the Scale-Invariant Features Transform (SIFT) method to detect features and create descriptors for them. These features are extracted for the ... See full document

10

Text Recognition using Convolutional Neural Network: A Review

... a network size of 189�160�36 was configured for this ...feedforward network was determined, the network was trained and tested in ...the network has not seen the testing data during the ... See full document

5

Gesture Recognition Using LFMCW Radar and Convolutional Neural Network

... gesture recognition technology plays an increasingly important role in various fields, especially in the non-contact and low-light ...gesture recognition system based on linear frequency modulation ... See full document

6

Facial expression recognition by using modified convolutional neural network (mcnn) and modified gabor filter

... is using the control parameters and averaging of the transformed to adjusted the features extrcted by Gbor filter and increse the accurcy of this ...is using the control parmeter to the equation 8 to adjust ... See full document

8

Recognition of Brahmi words by Using Deep Convolutional Neural Network

... CNN, achieving 77.01% [25] and 98.37% [26] accuracy, respectively, whereas Cecotti and Vajda [25] used 10,000 training datasets for 10 classes, i.e. 1,000 (average) samples for each type. Maitra, et al. [26] used 13,392 ... See full document

9

Developing A Face Recognition System Using Convolutional Neural Network

... Face recognition is a biometric method that uses features of face to identify ...face recognition is hard to handle due to pose invariant, illumination invariant and many ... See full document

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