scholarly journals LEARNING INVARIANT COLOUR FEATURES FOR PERSON REIDENTIFICATION

2020 ◽  
Vol 5 (5) ◽  
pp. 65-70 ◽  
Author(s):  
D.D. Chaudhary ◽  
Nikita Jadhav
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Body Part ◽  
Human Face ◽  
Direct Transformation ◽  
Colour Feature ◽  
Colour Features ◽  
Picture Element

In this examination we have proposed Learning invariant shading highlights for individual recognizable proof utilizing human face for high proficient flag exchange framework applications. In this paper, we have a tendency to propose an information driven approach for taking in shading designs from pixels examined from pictures crosswise over to camera sees. The instinct behind this work is that, even assuming picture element values of same colour would wander across views, they thought to be encoded with indistinguishable qualities. We tend to model colour feature age as a learning drawback by together learning a direct transformation and a wordbook to write in code picture component esteems. We have a tendency to conjointly dissect entirely unexpected estimating invariant shading zones. Abuse shading in light of the fact that the exclusively prompt, we tend to contrast our approach and all the estimating invariant shading zones and show better execution over every one of them. Overwhelming pivoted nearby double example is anticipated yields higher execution. This paper proposes a totally exceptional strategy of characterizing the outer body part misuse Convolutional Neural Network.

scholarly journals The Influence of Colour Features on Seed Identification Using Machine Vision

2016 ◽  
Vol 8 (1) ◽  
pp. 93-97 ◽  
Author(s):  
Sepideh ANVARKHAH ◽  
Ali Davari Edalat PANAH ◽  
Alireza ANVARKHAH
Keyword(s):  
Neural Network ◽  
Medicinal Plants ◽  
Medicinal Plant ◽  
Automatic System ◽  
Plant Seed ◽  
Average Accuracy ◽  
Colour Feature ◽  
Colour Features ◽  
Seed Identification ◽  
Total Average

Little studies have been done on morphology of medicinal plants seeds. This paper presents an automatic system for medicinal plant seed identification and evaluates the influence of colour features on seed identification. Six colour features (means of red, green and blue colours of the seed surface, as well as means of hue, intensity and saturation) were extracted by algorithm and applied as network input. Different combinations of colour features (one, two three, four, five and six colour features) were used to find out the most accurate combination for seed identification. Results showed that the six colour feature was the most accurate combination for seed identification (99.184% and 87.719% for training and test of neural network respectively). One colour feature had the lowest average accuracy values for seed identification (3.120% and 2.771%). In general, increasing the number of colour features increased the total average of accuracy values.

scholarly journals Deep Convolutional neural network (CNN) in tea leaf chlorophyll estimation: A new direction of modern tea farming in Assam, India

2021 ◽  
Vol 13 (3) ◽  
pp. 1059-1064
Author(s):  
Utpal Barman
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Light Condition ◽  
Absolute Error ◽  
Small Scale ◽  
K Nearest Neighbor ◽  
Leaf Chlorophyll ◽  
Colour Features ◽  
Tea Leaf

This study presents the uprising of leaf chlorophyll estimation from traditional mechanical method to machine learning-based method. Earlier chlorophyll estimation techniques such as Spectrophotometer and Soil Plant Analysis Development (SPAD) meter demand cost, time, labour, skill, and expertise. A small-scale tea farmer may not afford these devices. The present study reports a low-cost digital method to predict the tea leaf chlorophyll using 1-D Convolutional Neural Network (1-D CNN). After capturing the tea leaf images using a digital camera in a natural light condition, a total of 12 different colour features were extracted from tea leaf images. A SPAD was used to estimate the original chlorophyll value of the tea leaves. The paper shows the correlation of original tea leaf chlorophyll with the extracted colour features of the tea leaf images. Apart from 1-D CNN, the Multiple Linear Regression (MLR) and K-Nearest Neighbor (KNN) were also applied to predict the tea leaf chlorophyll and compared their results with the 1-D CNN. The 1-D CNN model outperformed with an accuracy of 81.1%, Mean Absolute Error (MAE) of 3.01, and Root Mean Square Error (RMSE) of 4.18. The investigation system is very simple and cost-effective. It can be used in tea farming as a digital SPAD for faster and accurate leaf chlorophyll estimation in an easy way.

scholarly journals Deep convolutional neural network based facial expression identification using face-parsing method

2021 ◽  
Vol 309 ◽  
pp. 01123
Author(s):  
Raju Yadav Mothukupally ◽  
P Chandra Sekhar Reddy
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Layered Structure ◽  
Body Part ◽  
Deep Convolutional Neural Network ◽  
Classification Task ◽  
Face Expression ◽  
North American Country

Face parsing methodology may be a one amongst the advancements in pc vision that analyses the surface synthesis of the external body part, to amass bits of information on options needs correct pixel segmentation of various components of face like (mouth, nose, eyes etc.). Same means the analysis on feeling recognition plays a eventful role in communication and interactions of humanity and additionally relevant to psychological activities. Considering the disadvantage that totally different completely different components of face contain different quantity of knowledge for face expression and also the weighted perform are not an equivalent for various faces. In keeping with analysis, the image classification task ordinarily drives North American country to the notable Convolutional Neural Network (CNN) during which we tend to ar victimization VGG19 model. beyond exploring around however CNN, sometimes performs for greyscale photos, we tend to selected to start from 3 consecutive convolutional layers followed by a most pooling layer, basic exploit work for convolutional layer and “relu” is used, even as an analogous artefact pattern. The highlights to be known victimization the convolutional layer distended to 128 layers from thirty-two, it is suggestable that multi-layered structure (with increasing layers) that performs and results the most effective outcomes for the DNN model. At last, the CNN layer is 1st smoothened and afterwards expertise 2 many dense layers to reach the yield layer during which SoftMax activation perform is used for multiclass classification. We tend to victimization Cohn-Kanadre face expression dataset of seven expressions like contempt, anger, disgust, happiness, fear, disappointment and surprise.

scholarly journals HUMAN FACE DETECTION METHODS USING COMBINED CASCADE OF CLASSIFIERS

2014 ◽  
pp. 114-125
Author(s):  
Ihor Paliy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Detection Methods ◽  
Classification Error ◽  
Asymmetric Structure ◽  
Human Face ◽  
High Detection Rate ◽  
Structure Generation ◽  
Human Face Detection

The paper presents the improved human face detection method using the combined cascade of classifiers with the improved face candidates’ verification approach, as well as methods and algorithms for the verification level (convolutional neural network) structure generation and training. The combined cascade shows a high detection rate with a very small number of false positives and the proposed candidates’ verification approach is in almost 3 times faster in comparison with the classic verification scheme. The network’s structure generation method allows creating the sparse asymmetric structure of the convolutional neural network automatically. The improved training method uses the adaptive training examples ratio to obtain a trained network with a very low classification error for the positive examples.

scholarly journals Classification of Sad Emotions and Depression Through Images Using Convolutional Neural Network (CNN)

2021 ◽  
Vol 6 (1) ◽  
pp. 90
Author(s):  
Muhammad Fathur Prayuda
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Total Loss ◽  
Test Accuracy ◽  
Human Face ◽  
Unique Shape

The human face has various functions, especially in expressing something. The expression shown has a unique shape so that it can recognize the atmosphere of the feeling that is being felt. The appearance of a feeling is usually caused by emotion. Research on the classification of emotions has been carried out using various methods. For this study, a Convolutional Neural Network (CNN) method was used which serves as a classifier for sad and depressive emotions. The CNN method has the advantage of preprocessing convolution so that it can extract a hidden feature in an image. The dataset used in this study came from the Facial expression dataset image folders (fer2013) where the dataset used for classification was taken with a ratio of 60% training and 40% validation with the results of the trained model of 60% total loss and 68% test accuracy.

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