International Journal of Computer and Applications

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review

Fruits and vegetables quality evaluation using computer vision: A review

Shape is one of the most important features in Content Based Image Retrieval (CBIR). Many shape representations and retrieval methods exists. However, most of those methods either do not well represent shape or are difficult to do normalization (making matching hard). Among them, methods based Fourier descriptors (FD) achieve both well representation and well normalization. Different shape signatures have been exploited to derive FDs, however, FDs derived from different signatures can have significant different effect on the result of retrieval. In this paper, we build a Java retrieval framework to compare shape retrieval using FDs derived from different signatures. Common issues and techniques for shape representation and normalization are also analyzed in the paper. Data is given to show the retrieval result.

A comparative study on shape retrieval using Fourier descriptiors with different shape signatures

The purpose of this study was to develop a methodology for assessing fruit quality objectively using texture analysis based on Curvelet Transform. Being a multiresolution approach, curvelets have the capability to examine fruit surface at low and high resolution to extract both global and local details about fruit surface. The fruit images were acquired using a CCD color camera and guava and lemon were analyzed by experimentation. Textural measures based on curvelet transform such as energy, entropy, mean and standard deviation were used to characterize fruits’ surface texture. The discriminating powers of these features for fruit quality grading is investigated. The acquired features were subjected to classifiers such as Support Vector Machines (SVM) and Probabilistic Neural Networks (PNN) and the performance of classifiers was tested for the two category grading of fruits namely healthy and defected. The results showed that best SVM classification was obtained with an accuracy of 96%. The study concludes that curvelet based textural features gives promising insights to estimate fruit’s skin damages.

/pdf/automated-skin-defect-identification-system-for-fruit-58fwhbd4d1.pdf

Automated Skin Defect Identification System for Fruit Grading Based on Discrete Curvelet Transform

Any horticulture product is expected to meet the set quality standards in order to improve its market value. The mango is one of the most delicious tropical fruit from India. Exports from India account for 0.11 per cent of the total domestic production as against about 4 per cent in case of other exporting countries like Mexico, Philippines and Venezuela. Case study of mango fruit from Maharashtra, India is being considered here for size grading purpose. Images of mango are captured using CCD camera and size analysis is carried out using the MATLAB package. The paper discusses various size estimation metrics which are used as feature vectors for two classifiers namely Feed Forward Neural network (FFNN) and Support Vector Machines. The performance of these classifiers in grading mangoes according to CODEX size standards is presented. Experimental results show that Statistical method give an average size grading efficiency of 97% irrespective of classifiers for mango size grading.

/pdf/comparative-performance-evaluation-of-size-metrics-and-3b6z8swt4l.pdf

Comparative Performance Evaluation of Size Metrics and Classifiers in Computer Vision based Automatic Mango Grading

Mango is a tropical fruit of India which plays a major role in earning foreign currency by export. The export sector of India is paying attention towards it because of its commercial significance. Image has assorted inbuilt features which reflect its content such as color, texture, shape, and spatial relationship features, etc. How to organize and utilize these features effectively in agriculture era is a valuable research topic. Shape is a first quality factor to be considered by consumer while purchasing mango fruit. The purpose of this research work is to explore image processing algorithms and techniques to sort misshapen mango fruits based on their shape features. The developed algorithm would be first step in automated grading and sorting machines in export industries. It can provide a base for fully automatic grading system using computer vision. A shape based mango fruit sorting using computer vision is discussed in this paper. Shape features (Region based and contour based) are designed using Fourier Transform. Wavelet based descriptor is derived from basic Fourier transform to catch local shape details. A two layered radial basis neural network is developed to classify well formed and deformed mango fruits. The experiment results show performance comparison of all Fourier based shape descriptors. Wavelet Fourier Descriptor outperforms region based and contour based Fourier descriptor with classification efficiency of 89.83%.

/pdf/performance-comparison-of-fourier-transform-and-its-3qp5jvq503.pdf

Performance Comparison of Fourier Transform and Its Derivatives as Shape Descriptors for Mango Grading

Smart video surveillance is a IOT-based application as it uses Internet for various purposes. The proposed system intimates about the presence of any person in the premises, also providing more security by recording the activity of that person. While leaving the premises, user activates the system by entering password. System working starts with detection of motion refining to human detection followed by counting human in the room and human presence also gets notified to neighbor by turning on alarm. In addition, notification about the same is send to user through SMS and e-mail. The proposed system’s hardware implementation is supported by Raspberry Pi and Arduino board; on the other hand, software is given by OpenCV (for video surveillance) and GSM module (for SMS alert and e-mail notification). Apart from security aspect, system is intelligent enough to optimize power consumption wastage if user forgets to switch off any electronic appliances by customizing coding with specific appliances.

Implementation of IoT-Based Smart Video Surveillance System

The increase in population leads to increment in pollution and accident as well. Technological development makes great efforts to detect the accident and monitor the increasing pollution. This paper is based on enhancement in the smart vehicle system. Different units implemented in this paper which enhance the vehicular system. The main objective is to detect accidents in real time and minimize the response time of medical help. For accident avoidance, tire pressure is measured whereas in accident detection is implemented with the help of node MCU. MQ7 is used in order to monitor the pollution. The proposed system is useful in reducing the vehicular accidents and pollution monitoring will help to know the environmental status.

Suchitra Khoje

Papers

Automated Skin Defect Identification System for Fruit Grading Based on Discrete Curvelet Transform

Comparative Performance Evaluation of Size Metrics and Classifiers in Computer Vision based Automatic Mango Grading

Performance Comparison of Fourier Transform and Its Derivatives as Shape Descriptors for Mango Grading

Implementation of IoT-Based Smart Video Surveillance System

Vehicle Collision Detection and Avoidance with Pollution Monitoring System Using IoT