An efficient fuzzy deep learning approach to recognize 2D faces using FADF and ResNet-164 architecture

2021 ◽  
pp. 1-10
Author(s):  
K. Seethalakshmi ◽  
S. Valli
Keyword(s):  
Deep Learning ◽  
Edge Detection ◽  
Error Rate ◽  
Recognition Rate ◽  
Learning Approach ◽  
Noise Detection ◽  
Accuracy Rate ◽  
Automatic Feature Extraction ◽  
Morphological Gradient ◽  
Smoothing Effects

Deep learning using fuzzy is highly modular and more accurate. Adaptive Fuzzy Anisotropy diffusion filter (FADF) is used to remove noise from the image while preserving edges, lines and improve smoothing effects. By detecting edge and noise information through pre-edge detection using fuzzy contrast enhancement, post-edge detection using fuzzy morphological gradient filter and noise detection technique. Convolution Neural Network (CNN) ResNet-164 architecture is used for automatic feature extraction. The resultant feature vectors are classified using ANFIS deep learning. Top-1 error rate is reduced from 21.43% to 18.8%. Top-5 error rate is reduced to 2.68%. The proposed work results in high accuracy rate with low computation cost. The recognition rate of 99.18% and accuracy of 98.24% is achieved on standard dataset. Compared to the existing techniques the proposed work outperforms in all aspects. Experimental results provide better result than the existing techniques on FACES 94, Feret, Yale-B, CMU-PIE, JAFFE dataset and other state-of-art dataset.

scholarly journals Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 982 ◽  
Author(s):  
Hyo Lee ◽  
Ihsan Ullah ◽  
Weiguo Wan ◽  
Yongbin Gao ◽  
Zhijun Fang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Recognition Rate ◽  
Experimental Results ◽  
Learning Approach ◽  
Deep Model ◽  
Proposed Model ◽  
Real Time Applications ◽  
Model Recognition

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

scholarly journals Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach

2021 ◽  
Vol 3 (2) ◽  
pp. 133-149
Author(s):  
Akey Sungheetha ◽  
Rajesh Sharma R
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Deep Learning ◽  
Visual Recognition ◽  
Research Work ◽  
Recognition Rate ◽  
Remote Sensing Image ◽  
Quality Data ◽  
Learning Approach ◽  
Remote Sensing Images

Over the last decade, remote sensing technology has advanced dramatically, resulting in significant improvements on image quality, data volume, and application usage. These images have essential applications since they can help with quick and easy interpretation. Many standard detection algorithms fail to accurately categorize a scene from a remote sensing image recorded from the earth. A method that uses bilinear convolution neural networks to produce a lessweighted set of models those results in better visual recognition in remote sensing images using fine-grained techniques. This proposed hybrid method is utilized to extract scene feature information in two times from remote sensing images for improved recognition. In layman's terms, these features are defined as raw, and only have a single defined frame, so they will allow basic recognition from remote sensing images. This research work has proposed a double feature extraction hybrid deep learning approach to classify remotely sensed image scenes based on feature abstraction techniques. Also, the proposed algorithm is applied to feature values in order to convert them to feature vectors that have pure black and white values after many product operations. The next stage is pooling and normalization, which occurs after the CNN feature extraction process has changed. This research work has developed a novel hybrid framework method that has a better level of accuracy and recognition rate than any prior model.

Automatic feature extraction in large fusion databases by using deep learning approach

2016 ◽  
Vol 112 ◽  
pp. 979-983 ◽  
Author(s):  
Gonzalo Farias ◽  
Sebastián Dormido-Canto ◽  
Jesús Vega ◽  
Giuseppe Rattá ◽  
Héctor Vargas ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Learning Approach ◽  
Automatic Feature Extraction

scholarly journals Deep Learning-Based Character Gesture Recognition

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Husam Al-Behadili ◽  
Alaa H. Ahmed ◽  
Hasan M.A. Kadhim
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Kinect Sensor ◽  
Accuracy Rate ◽  
Hand Detection ◽  
Detection Algorithms ◽  
Text Input ◽  
Input Method ◽  
Single Sensor

The article describes a new text input method based on gesture recognition, which enables direct physical-to-digital text input. This enables hand-free and in-air writing without any need for keyboards, mice, etc. This is done with the help of state-of-the-art deep learning methods and a single Kinect sensor. The authors were able to achieve a high-accuracy recognition rate by using any wearable device, in contrast to the existing methods, and utilizing a single sensor. Furthermore, among several existing deep learning structures, the authors determined that the best deep learning structure suitable for the character-based gesture data is the DenseNet Convolutional neural network. For instance, the training loss curve shows that DenseNet has the fastest converging curve compared to the others despite maintaining the highest accuracy rate. Our proposed method allows for the improvement of the recognition rate from 96.6% (in the existing algorithms) to 98.01% when the DenseNet structure is used despite using only a single sensor instead of multiple cameras. The use of the Kinect sensor not only reduces the number of cameras to one but also overrides the necessity for any additional hand detection algorithms. These results aid in improving the speed and the efficiency of the character-based gesture recognition. The proposed system can be used in applications that require accurate decision making, such as in operation rooms.

scholarly journals Deep Learning Approach for Apple Edge Detection to Remotely Monitor Apple Growth in Orchards

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 26911-26925 ◽  
Author(s):  
Dandan Wang ◽  
Changying Li ◽  
Huaibo Song ◽  
Hongting Xiong ◽  
Chang Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Edge Detection ◽  
Learning Approach

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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