The purpose of this paper is to offer a machine vision approach for classifying cocoa beans based on their morphological properties. Using traditional machine learning approaches, the shape and size of cocoa beans were retrieved from photographs. A series of image processing techniques are used to extract the features from the photos. Finally, typical machine learning approaches such as KNN, SVM, Decision Tree, and Random Forest are used to divide the cocoa beans into four groups: large, medium, small, and rejected. A comparison of different methodologies is also carried out. Two optimization strategies, Univariate Selection and Feature Importance, are used to maximize retrieved features prior to training the model. For performance analysis, trained models are evaluated using stratified K-fold cross validations and the mean cross validation score is produced. The Random Forest Classifier has the greatest accuracy score of 0.75, according to the results of the experiments. Keywords: Cocoa beans, Classification, Image processing, Machine Learning, Feature Optimization.

Image information maximization is an alternative method of contrast enhancement of images. There are plenty of algorithms for contrast enhancement of poor illumination images. In present paper we have proposed a novel method of psycho-visual evaluation of contrast enhancement algorithms. Adaptive Neuro-Fuzzy Inference System (ANFIS) is used here for classification of well known contrast enhancement algorithms. The metric/feature of contrast enhancement is modeled including image statistics both in spatial and frequency domain. The perception inspired model is then used for automatic classification of algorithms depending on the strength of contrast enhancement.

Computerized human face recognition is a complex task of deformable pattern recognition. The principal source of complexities lies in the significant inter-class overlapping of faces due to the variations caused by different poses, illuminations, and expressions (PIE). Elastic Bunch Graph Matching (EBGM.) is a feature-based face recognition algorithm which has been used fairly reliably to determine facial attributes from an image. It extracts the texture using Gabor wavelets around a set of biometric landmark points on a face, and generates a level graph. One of the degrading factor of the performance of EBGM based face recognition system is the size of the database, particularly when the database size is in tuned to millions, the performance of FRE falls drastically. In the present paper data mining approach is presented to improve the performance of the EBGM based face recognition engine in case of large database. We have proposed entropy based decision tree for feature selection and feature hierarchy. The selected features are taken to form suitable feature vector for Fuzzy C-means clustering. The clustered set becomes the reduced search space for the query face. Improvement in the performance of the EBGM based FRE is presented with suitable experimental results.

This paper shows how the most important features can be selected from the face so that the performance of any face recognition engine can be improved by matching only the maximally distinguishable features. Creating an automated face recognition system that can duplicate human performance in recognizing a face is one of the key goal of computer vision researchers. So, it is necessary that computational researchers should know the key findings from a facial image. Here the feature hierarchy in accordance with importance to recognize a face is used in our Face Recognition system and it is observed that the performance have been improved drastically after selecting the mostly contributing feature set.