scholarly journals Inconsistent Cluster Analysis With Disease Feature Enhancement (ICADFE) For American Cotton Leaf Disease Recognition

2019 ◽  
Vol 8 (5C) ◽  
pp. 1497-1505
Keyword(s):  
Cluster Analysis ◽  
Cotton Plant ◽  
Plant Disease ◽  
Feature Descriptor ◽  
Cotton Leaf ◽  
Detection Accuracy ◽  
Dynamic Feature ◽  
Feature Enhancement ◽  
Speeded Up Robust Features ◽  
Feature Descriptors

The broad leaves of cotton plant carry various visible disease symptoms. The ability of visual analysis by experts motivated the development of the plant disease recognition model. There are several visual feature descriptors, which can be primarily distinguished on the basis of pattern, texture or color. This system has been developed for the convenience of the farmers, who can avail the benefit by submitting the pictures of infected cotton leaves on the interface and the plant disease recognition system will return type of disease. In this paper, a dynamic feature descriptor is designed with inconsistent cluster analysis (ICA) and disease feature enhancement (DFE), which are combined as hybrid descriptor known as ICADFE for the recognition of the cotton plant disease. The ICADFE is found to improve the detection accuracy (approx. 80%), precision (approx 95%) and f1-measure (approx. 88%) on average in comparison with traditional shape and texture based feature descriptors such as scale invariant feature transform (SIFT), speeded up robust features (SURF) and fast retina keypoints (FREAK) with multicategory SVM (mSVM) for disease recognition

An Evolutionary Computing Approach to Solve Object Identification Problem in Image Processing Applications

2020 ◽  
Vol 17 (1) ◽  
pp. 439-444 ◽  
Author(s):  
Katamneni Vinaya Sree ◽  
G. Jeyakumar
Keyword(s):  
Object Detection ◽  
Identification Problem ◽  
Object Identification ◽  
Feature Descriptor ◽  
Detection Accuracy ◽  
Optimum Number ◽  
De Algorithm ◽  
Feature Descriptors ◽  
General Object ◽  
The Given

In the given image identifying the existence of a required object is the concern of the object detection process. This is quite natural for Human without any effort, however making a machine to detect an object in image is tedious. To make machines to recognize the objects, the feature descriptor algorithms are to be implemented. The general object detection approaches use collection of local and global descriptors to represent an image. Difficulties arise during this process when there is variation in lightening, positioning, rotation, mirroring, occlusion, scaling etc., of the same object in different image scenes. To overcome these difficulties, we need combination of features that detects the object in the image scene. But there exist lot of descriptors that can be used. Hence, finding the required number of feature descriptors for object detection is a crucial task. The question that comes out here is how to select the optimum number of descriptors to achieve optimum accuracy? The answer for the question is an optimization algorithm, which can be employed to select the best combination of the descriptors with maximum detection accuracy. This paper proposing an Evolutionary Computation (EC) based approach with the Differential Evolution (DE) algorithm to find the optimal combination of feature descriptors to achieve optimal object detection accuracy. The proposed approach is implemented and its superiority is verified with four different images and results obtained are presented in this paper.

scholarly journals PERFORMANCE ANALYSIS OF SELECTED FEATURE DESCRIPTORS USED FOR AUTOMATIC IMAGE REGISTRATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 559-566
Author(s):  
O. G. Ajayi
Keyword(s):  
Image Registration ◽  
Feature Descriptor ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Feature Descriptors ◽  
Single Feature ◽  
Image Registration Algorithm ◽  
Maximally Stable Extremal Region ◽  
Registration Scheme ◽  
Scale Invariant Feature

Abstract. Automatic detection and extraction of corresponding features is very crucial in the development of an automatic image registration algorithm. Different feature descriptors have been developed and implemented in image registration and other disciplines. These descriptors affect the speed of feature extraction and the measure of extracted conjugate features, which affects the processing speed and overall accuracy of the registration scheme. This article is aimed at reviewing the performance of most-widely implemented feature descriptors in an automatic image registration scheme. Ten (10) descriptors were selected and analysed under seven (7) conditions viz: Invariance to rotation, scale and zoom, their robustness, repeatability, localization and efficiency using UAV acquired images. The analysis shows that though four (4) descriptors performed better than the other Six (6), no single feature descriptor can be affirmed to be the best, as different descriptors perform differently under different conditions. The Modified Harris and Stephen Corner Detector (MHCD) proved to be invariant to scale and zoom while it is excellent in robustness, repeatability, localization and efficiency, but it is variant to rotation. Also, the Scale Invariant feature Transform (SIFT), Speeded Up Robust Features (SURF) and the Maximally Stable Extremal Region (MSER) algorithms proved to be invariant to scale, zoom and rotation, and very good in terms of repeatability, localization and efficiency, though MSER proved to be not as robust as SIFT and SURF. The implication of the findings of this research is that the choice of feature descriptors must be informed by the imaging conditions of the image registration analysts.

scholarly journals Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xing Hu ◽  
Shiqiang Hu ◽  
Xiaoyu Zhang ◽  
Huanlong Zhang ◽  
Lingkun Luo
Keyword(s):  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Computation Time ◽  
Compact Representation ◽  
Feature Descriptor ◽  
Neighbor Distance ◽  
Low Level ◽  
Feature Descriptors ◽  
Nearest Neighbor Distance ◽  
Crowded Scenes

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.

scholarly journals Carbohydrate and Amino Acid Profiles of Cotton Plant Biomass Products

Agriculture ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 2 ◽  
Author(s):  
Zhongqi He ◽  
Dan C. Olk ◽  
Haile Tewolde ◽  
Hailin Zhang ◽  
Mark Shankle
Keyword(s):  
Amino Acids ◽  
Cotton Plant ◽  
Management Practices ◽  
Plant Biomass ◽  
Plant Residues ◽  
Cotton Leaf ◽  
Essential Information ◽  
Plant Parts ◽  
Processing Strategies ◽  
Biomass Materials

To achieve the optimal and diverse utilization of cotton (Gossypium hirsutum) plant residues in various agricultural, industrial, and environmental applications, the chemical composition of cotton biomass tissues across different plant parts (e.g., seed, boll, bur, leaves, stalk, stem, and root) is of essential information. Thus, in this work, we collected field-grown whole mature cotton plants and separated them into distinct biomass fractions including main stems, leaf blades, branches, petioles, roots, and reproductive parts (mid-season growth stage) or bur, peduncles/bract, and seed cotton (pre-defoliation stage). The contents of selected carbohydrates and amino acids in these cotton biomass materials were determined. Both essential and nonessential amino acids were enriched in cotton leaf blades and reproductive parts. The distribution pattern of the selected carbohydrates differed from that of amino acids—higher contents of carbohydrate were found in roots, main stems, and branches. Although glucose was the most abundant non-structural carbohydrate in cotton plant parts at mid-season, xylose was the most abundant in most plant parts at the pre-defoliation stage. Nutritional carbohydrates and amino acids were further accumulated in seeds at pre-defoliation. The information reported in this work would be helpful in exploring and optimizing management practices and processing strategies for utilizing cotton crop biomass materials as valuable and renewable natural resources.

A Novel Hybrid Discrete Cosine Transform Speeded Up Robust Feature-Based Secure Medical Image Watermarking Algorithm

2020 ◽  
Vol 10 (11) ◽  
pp. 2588-2599
Author(s):  
Saqib Ali Nawaz ◽  
Jingbing Li ◽  
Uzair Aslam Bhatti ◽  
Anum Mehmood ◽  
Raza Ahmed ◽  
...  
Keyword(s):  
Discrete Cosine Transform ◽  
Digital Watermarking ◽  
Copyright Protection ◽  
Image Watermarking ◽  
Image Feature ◽  
Feature Descriptor ◽  
Cosine Transform ◽  
Speeded Up Robust Features ◽  
Feature Based ◽  
Dct Coefficients

With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. We design and implement a meaningful binary watermark embedding and extraction algorithm based on the SURF feature descriptor and discrete-cosine transform domain digital image watermarking algorithm. The algorithm firstly uses the affine transformation with a feature matrix and chaotic encryption technology to preprocess the watermark image, enhance the confidentiality of the watermark, and perform block and DCT coefficients extraction on the carrier image, and then uses the positive and negative quantization rules to modify the DCT coefficients. The embedding of the watermark is completed, and the blind extraction of the watermark realized. Correlation values are more than 90% in most of the attacks. It provides better results against different noise attacks and also better performance against rotation. Transparency and high computational efficiency, coupled with dual functions of copyright protection and content authentication, is the advantage of the proposed algorithm.

Feature-Based GDLOH Deformable Registration for CT Lung Image

2013 ◽  
Vol 333-335 ◽  
pp. 969-973
Author(s):  
Yu Han Yang ◽  
Yao Qin Xie
Keyword(s):  
Feature Detection ◽  
Deformable Registration ◽  
Measurement Tool ◽  
Feature Descriptor ◽  
Feature Points ◽  
Scale Invariant ◽  
Clinical Images ◽  
Speeded Up Robust Features ◽  
Matching Process ◽  
Novel Approach

To improve the efficiency and accuracy of the conventional SIFT-TPS (Scale-invariant feature transform and Thin-Plate Spline) method in deformable registration for CT lung image, we develop a novel approach by using combining SURF(Speeded up Robust Features) and GDLOH(Gradient distance-location-orientation histogram) to detect matching feature points. First, we employ SURF as feature detection to find the stable feature points of the two CT images rapidly. Then GDLOH is taken as feature descriptor to describe each detected points characteristic, in order to supply measurement tool for matching process. In our experiment, five couples of clinical images are simulated using our algorithm above, result in an obvious improvement in run-time and registration quality, compared with the conventional methods. It is demonstrated that the proposed method may create a new window in performing a good robust and adaptively for deformable registration for CT lung tomography.

scholarly journals Plant Disease Detection and Monitoring Using Artificial Neural Network

2022 ◽  
Vol 10 (01) ◽  
pp. 715-722
Author(s):  
Stella I. Orakwue ◽  
Nkolika O. Nwazor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Crop Production ◽  
Plant Disease ◽  
Disease Detection ◽  
Detection Accuracy ◽  
Major Threat ◽  
The Neural Network ◽  
Artificial Neural ◽  
Hyperspectral Sensor

Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested validated and the result showed a fungi detection accuracy of 96.61% and the percentage increment was 19.53%.

scholarly journals Detection of Cotton Leaf Disease Using Image Processing Techniques

2021 ◽  
Vol 2062 (1) ◽  
pp. 012009
Author(s):  
Sushreeta Tripathy
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Region Of Interest ◽  
Texture Features ◽  
Disease Diagnosis ◽  
Cotton Leaf ◽  
Diagnosis System ◽  
Image Processing Techniques ◽  
Plant Disease Diagnosis ◽  
Processing Techniques

Abstract In the area of research, diagnosis of disease symptoms in the plants duly applying image processing methods is a matter of big concern. The need of the hour is to prepare an efficient plant disease diagnosis system that can help the farmers in their cultivation and farming. This work is an attempt to prepare a framework of plant disease diagnosis system by using the cotton plant leaves. The digital pictures of cotton leaves are obtained to undergo a set of image processing techniques. Thresholding based segmentation techniques are used to remove the region of interest (ROI) i.e., infected part from the enhanced images. Consequently, diseases are detected from the region of interest by using an accurate set of visual texture features. At last treatment actions are taken to supervise the diseases found in the plants. This work will help the farmer’s society to take effective measures to protect their crops from diseases.

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