Application of pattern recognition

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1 Application of pattern recognition Dr. K.Vijayarekha Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur Joint Initiative of IITs and IISc Funded by MHRD Page 1 of 6

2 Table of Contents 1. Application of pattern recognition... 3 Joint Initiative of IITs and IISc Funded by MHRD Page 2 of 6

3 1. Application of pattern recognition Application of pattern recognition problem for classification of external defects of citrus fruits based on the images Quality of any product determines its sale value in the market. Visual input to the consumer plays a very important role in increasing the acceptance level prior to the decision taken for the purchase. The quality is determined by various factors like color, size and shape of the products. Agricultural produces like fruits, vegetables and food grains are graded into quality categories before being packed or before being sent for process line.traditional grading and sorting involves humans, which is time consuming, less accurate, inconsistent, and subjective and may cause eye fatigue and is subject to sorting errors due to different judgments by different persons. With increasing pressure placed on industry to increase efficiency, to improve quality and to reduce cost, the need for more flexible and intelligent inspection system is growing day by day. Automatic grading and sorting incorporates machine vision technology encompassing the image processing techniques. Machine vision applications make use of the images of the products being graded, which forms a non-destructive way of testing. Imaging can be done in any of the bands of the electro magnetic spectrum. Nondestructive testing of food crops like automatic grading and sorting with machine vision technology minimizes the wastage of products and time taken for testing. It is proved to yield better results than the traditional method of manual grading and sorting. Normally, automatic grading and sorting involves in the development of algorithms for the surface defect detection, which includes modules such as image acquisition, defect segmentation, feature extraction and defect classification based on the extracted features. Hence image-processing systems play an important role in the food quality evaluation by maintaining accuracy and consistency while eliminating the subjectivity of manual inspections. The expectation for improved quality especially in consumer food products is increasing day by day due to the sophisticated life style and increased awareness of the consumers. This in turn increases the need for the enhanced quality monitoring in the packing stage itself. Agricultural produces decay quickly if kept for a long period. This results in external and internal defects. Joint Initiative of IITs and IISc Funded by MHRD Page 3 of 6

4 Sometimes, fruits get contaminated with pathogens when they come in contact with ground which may have animal fecal material or ingesta of an animal s gastro intestinal tracts and cause serious effects if consumed directly. Some pathogens developed under such conditions do not die even during processing and hence cause diseases, which may be fatal. These unhealthy produces must be removed from the process line, which otherwise will cause serious health hazards. Fruits with diseased or fungal contaminated surfaces, open skin cuts and bruises may become sites for decay and bacterial growth. Developing detection technologies for fruit defects and contaminations in the post harvest pre processing stage is thus very important for quality and especially for safety. The citrus fruit defects and diseases may be due to post harvest handling or due to peel disorders. The major post harvest diseases are diplodia and phomopsis stem-end rot, splitting, pitting, green and blue mold, sour and brown rots, anthracnose, and alternaria rot etc. Among the various defects reported, we could resource and procure mandarin fruits with three types of defects viz. pitting, splitting and stem end rot. Fig. 1.Citrus fruit images having pitting, splitting and stem-end rot defects Pitting is caused due to oil gland collapse of many small circular pits associated with mechanical damage or reduced gas exchange. Pits can coalesce to form irregular patches and brown to black blemishes. Splitting is caused due to the inability of the outer skin of the fruit to hold the weight of the fruit. The outer skin of the fruit splits and the inner fruit gets exposed. Unlike the other types of the defects, the defective region is brighter compared with the healthy tissues. Stem-end rot is of two types, viz. phomopsis stem end rot and diplodia stem end rot. In the initial stages of the infection, both types of stem end rot are similar. Joint Initiative of IITs and IISc Funded by MHRD Page 4 of 6

5 With phomopsis stem-end rot the infected tissue shrinks the affected area, which becomes tan to dark brown and a clear line of demarcation is formed at the junction between diseased and healthy skin. Machine vision is the use of digital imaging for measurement, inspection and control and is very useful in automatic grading and sorting of fruits. Machine-vision applications use a material s optical properties to provide information that can define the characteristics of the material. For food products and biological materials, the optical properties may be related to the product s physical characteristics, such as size, shape, projected area, etc. Machine vision is a unique sensing method because excitation energy can be regulated and even structured in a pattern to get a response from the biological material in the scene. The primary elements of a machine-vision sensing system are the scene, the illumination system, the image of the scene, a description of the scene formed from the image and an application response to the description formed from the image. The illumination system provides electromagnetic radiation to excite the image sensor to form an image. If machine vision technique employing the image processing methods is applied for defect classification of fruits, the accuracy and the percentage of classification can be improved to a greater extent. The defects do appear on the images of the fruits, which are due to the differences in reflectances of the normal surface from the defective region and hence can be identified using image analysis. Image analysis in agricultural application consists of three steps (1) Image enhancement (2) Image feature extraction (3) Image feature classification. Image enhancement is commonly applied to a digital image to correct problems like poor contrast or noise. Image enhancement procedures such as morphological operations, filtering operations, and pixel-to-pixel operations are generally used to correct inconsistencies in the acquired images caused by inadequate and non-uniform illumination. An image feature is a distinguishing primitive characteristic or attribute of an image. It can also be defined as any extractable measurement. Some features are natural in the sense that such features are defined by the visual appearance of an image, while other artificial features result from specific manipulations of an image. Image features are of major importance in the isolation of regions of common property within an image (image segmentation) and subsequent identification or labeling of such regions (Image classification). Joint Initiative of IITs and IISc Funded by MHRD Page 5 of 6

6 Image classification analyzes the numerical properties of various image features and organizes them into categories. Classification algorithms typically employ two phases of processing: training and testing. In the initial training phase, characteristic properties of typical image features are isolated into different feature space partitions. Based on these, a unique description of each classification category, i.e. training class, is created. In the subsequent testing phase, these feature-space partitions are used to classify image features. Joint Initiative of IITs and IISc Funded by MHRD Page 6 of 6

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