Principle Component Analysis

To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved.

https://www.mdpi.com/1424-8220/17/7/1474/pdf

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm.

This paper introduces a Genetic Algorithm (GA) based optimization for rule base and scaling factors adjustment to enhance the performance of fuzzy logic controllers. First a recursive rule base adjustment algorithm is developed, which has the benefit that it is computationally more efficient for the generation of a decision table. Then utilizing the advantage of GA optimization, a novel approach that each random combination of the optimized parameters (including the membership function selection for the rule base and controller scaling factors) is coded into a Real Coded string and treated as a chromosome in genetic algorithms is given. The optimization for rule base with the correspondent membership function and scaling factors using GA is easy to be realization in engineering. Simulation results are presented to support this thesis.

Computational Intelligence

Artificial bee colony (ABC) algorithm is a swarm intelligence algorithm, which simulates the foraging behavior of honeybees. It has been successfully applied to many optimization problems in different areas. Since 2009, ABC algorithm has been employed for various problems in signal, image, and video processing fields. This paper presents the problems ABC algorithm has been applied in these fields and describes how ABC algorithm was used in the approaches for solving these kinds of problems.

A survey on the applications of artificial bee colony in signal, image, and video processing

The recently proposed dithering techniques could substantially improve measurement quality when fringes are wide, but offer limited improvement when fringes are narrow. This Letter presents a genetic algorithm to optimize the dithering technique for sinusoidal structured pattern representation. We believe both simulation and experimental results show that this proposed algorithm can substantially improve fringe quality for both narrow and wide fringe patterns.

https://engineering.purdue.edu/ZhangLab/publications/papers/2013-ol-genetic.pdf

Genetic method to optimize binary dithering technique for high-quality fringe generation

The gray level co-occurrence matrix (GLCM) is widely used for textural feature extraction. The features obtained from GLCM matrix are subjected to the classifiers for the purpose of identification and classification. In this paper the combinations between different features, obtained from GLCM matrix, are studied. For experiments, the leaves of medicinal plants are considered, as proper identification of medicinal plant is crucial for appropriate utilization of their medicinal values. Two popular Indian medicinal plants, namely, Neem and Tulsi have been considered for classification using back propagation multi layer perceptron (BP-MLP) neural network classifier. Beside combination the further classification improvements may be achieved using different preprocessing techniques, which have also been experimented. The results show that preprocessed combined GLCM features can provide higher classification rate compared to raw single GLCM features.

Study on the potential of combined GLCM features towards medicinal plant classification

Face recognition is an active research area in the field of computer vision and pattern recognition. Despite of considerable research and developments a time and memory efficient method that is robust to different factors e.g. facial expressions, pose, illumination, age, etc. remains a major challenges. This paper presents expressions invariant face recognition by detecting the fiducial points and employing speeded up robust feature (SURF) along with Gabor filter. The presented method is tested with test images with different expressions and found to be a better performer over the conventional SURF algorithm.

Expressions invariant face recognition using SURF and Gabor features

Towards optimized binary pattern generation for grayscale digital halftoning: A binary particle swarm optimization (BPSO) approach

Medicinal plants are getting increasingly popular across the world for their ability to cure different diseases including chronic ones. The chemical compositions present in those plant leaves are main contributors for the healing characteristics. The potential of using such plants also depends on the maturity of the medicinal plant under use. The leaves with appropriate maturity can cause better healing potential. This paper presents a computer vision based approach towards identification of medicinal leaves namely Kalmegh and Tulsi against the different maturity levels. The morphological features from the processed images of leaves with different maturity levels are extracted in this work. The feature sets are subjected to Principal Component Analysis (PCA) based identification and separability measures for identification purpose. The results show that the presented morphological feature based maturity identification can be a promising method.

Morphological feature based maturity level identification of Kalmegh and Tulsi leaves

Medicinal plants are used to cure different common and chronic diseases in different Asian countries including India. The easy availability and planting possibility make them popular resources for alternative medicinal practices like Ayurveda. These medicinal plants possess proven healing potential without causing any side effects. The biochemical constituents of the medicinal leaves are the fundamental reasons of their healing power which considerably vary with maturity. The existing practices of maturity detection are largely based on chemical analysis of leaves through different instruments which are expensive, invasive in nature and time consuming. This paper reports a computer vision-based system to classify the medicinal leaves along with the corresponding maturity level. The process reported is advantageous in terms of comparatively easier, faster, less expensive and non-invasive operations. The presented system captures the leaf images in controlled illumination and processed leaf images are fed to the convolutional neural network (CNN) architecture-based system for classification of both the type of leaves and maturity stage. The paper also presents application of binary particle swarm optimization ((BPSO) for arriving the values of the CNN hyperparameters. The potential of the system has been assessed with three popular species of medicinal plants, namely Neem, Tulsi and Kalmegh. The classification results were verified with repeated test runs and different standard metrics including tenfold cross-validation method. The paper shows that the presented CNN-driven computer vision framework can provide about 99% classification accuracy for simultaneous prediction of leaf specie and maturity stage. Such significant performance of the presented method can be considered as a potential addition to the existing chemical and instrumental methods.

A convolutional neural network-driven computer vision system toward identification of species and maturity stage of medicinal leaves: case studies with Neem, Tulsi and Kalmegh leaves

Arpitam Chatterjee

Papers

Study on the potential of combined GLCM features towards medicinal plant classification

Expressions invariant face recognition using SURF and Gabor features

Towards optimized binary pattern generation for grayscale digital halftoning: A binary particle swarm optimization (BPSO) approach

Morphological feature based maturity level identification of Kalmegh and Tulsi leaves

A convolutional neural network-driven computer vision system toward identification of species and maturity stage of medicinal leaves: case studies with Neem, Tulsi and Kalmegh leaves