Anand Singh Jalal

A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases

is a base algorithm for density based clustering. It can detect the clusters of different shapes and sizes from the large amount of data which contains noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. In this paper, we propose a density varied DBSCAN algorithm which is capable to handle local density variation within the cluster. It calculates the growing cluster density mean and then the cluster density variance for any core object, which is supposed to be expended further, by considering density of its -neighborhood with respect to cluster density mean. If cluster density variance for a core object is less than or equal to a threshold value and also satisfying the cluster similarity index, then it will allow the core object for expansion. The experimental results show that the proposed clustering algorithm gives optimized results.

Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns

Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps, in the first step K-Means clustering technique is used for the image segmentation, in the second step some state of the art features are extracted from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of apple fruit diseases. The classification accuracy for the proposed solution is achieved up to 93%.

An Enhanced Density Based Spatial Clustering of Applications with Noise

DBSCAN is a pioneer density based clustering algorithm. It can find out the clusters of different shapes and sizes from the large amount of data which is containing noise and outliers. But the clusters detected by it contain large amount of density variation within them. It can not handle the local density variation that exists within the cluster. For good clustering a significant density variation may be allowed within the cluster because if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper we propose an Enhanced DBSCAN algorithm which keeps track of local density variation within the cluster. It calculates the density variance for any core object with respect to its e -neighborhood. If density variance of a core object is less than or equal to a threshold value and also satisfying the homogeneity index with respect to its e -neighborhood then it will allow the core object for expansion. The experimental results show that the proposed clustering algorithm gives optimized results.

Adapted Approach for Fruit Disease Identification using Images

Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, an adaptive approach for the identification of fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps; in the first step K-Means clustering technique is used for the defect segmentation, in the second step some state of the art features are extracted from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. We have considered diseases of apple as a test case and evaluated our approach for three types of apple diseases namely apple scab, apple blotch and apple rot. Our experimental results express that the proposed solution can significantly support accurate detection and automatic identification of fruit diseases. The classification accuracy for the proposed solution is achieved up to 93%.

Species and variety detection of fruits and vegetables from images

Efficient detection of ‘species and variety’ of fruits and vegetables from the images is one of the major challenges for the computers. In this paper, we introduce a framework for the fruit and vegetable classification problem which takes the images of fruits and vegetables as input and returns it is species and variety. The input image contains fruit or vegetable of single variety in arbitrary position and in any number. This paper also introduces a texture feature based on sum and difference of intensity values of the neighbouring pixels of the colour images. The experimental results show that the proposed texture feature supports accurate fruit and vegetable recognition and performs better than other state-of-the-art colour and texture features. The classification accuracy for the proposed ISADH texture feature is achieved up to 99%.

Fruit disease recognition using improved sum and difference histogram from images

Diseases in fruit cause devastating problem in production and availability. The classical approach of fruit disease recognition is based on the naked eye observation by experts. Detection of defects is still problematic due to the natural variability of colour in different types of fruits, high variance of defect types, and presence of stem/calyx. In this paper, a framework for the recognition of fruit diseases is proposed. The proposed approach is composed of the following three main steps; defect segmentation, feature extraction, and classification. This paper also introduces an improved sum and difference histogram (ISADH) texture feature based on the intensity values of the neighbouring pixels. The gradient filters are also used with ISADH in this paper to boost the discriminative ability. We have considered apple diseases as a test case and evaluated our program. Experimental results suggest that the proposed method can significantly support automatic recognition of fruit diseases. The classification accuracy has achieved more than 97% using ISADH texture feature. Our method is able to achieve nearly 99.9% of accuracy in conjunction with the gradient filters.

Recognition of Indian Sign Language using feature fusion

Sign Language is the most natural and expressive way for the hearing impaired. This paper presents a method for automatic recognition of two handed signs of Indian Sign Language (ISL). The method consists of three phases: Segmentation, Feature Extraction and Recognition. The segmentation is done through Otsus algorithm. In the feature extraction phase, shape descriptors, HOG descriptors (Histogram of Oriented Gradient) and SIFT (Scale Invariant Feature Transform) feature have been fused to compute a feature vector. In the recognition phase, a multi-class Support Vector Machine (MSVM) is used for training and classifying signs of ISL. The experimental results provide evidence of the effectiveness of the proposed approach with 93% recognition rate.

LSB based non blind predictive edge adaptive image steganography

Image steganography is the art of hiding secret message in grayscale or color images. Easy detection of secret message for any state-of-art image steganography can break the stego system. To prevent the breakdown of the stego system data is embedded in the selected area of an image which reduces the probability of detection. Most of the existing adaptive image steganography techniques achieve low embedding capacity. In this paper a high capacity Predictive Edge Adaptive image steganography technique is proposed where selective area of cover image is predicted using Modified Median Edge Detector (MMED) predictor to embed the binary payload (data). The cover image used to embed the payload is a grayscale image. Experimental results show that the proposed scheme achieves better embedding capacity with minimum level of distortion and higher level of security. The proposed scheme is compared with the existing image steganography schemes. Results show that the proposed scheme achieves better embedding rate with lower level of distortion.

Application of Image Processing in Fruit and Vegetable Analysis: A Review

Abstract Images are an important source of data and information in the agricultural sciences. The use of image-processing techniques has outstanding implications for the analysis of agricultural operations. Fruit and vegetable classification is one of the major applications that can be utilized in supermarkets to automatically detect the kinds of fruits or vegetables purchased by customers and to determine the appropriate price for the produce. Training on-site is the underlying prerequisite for this type of arrangement, which is generally caused by the users having little or no expert knowledge. We explored various methods used in addressing fruit and vegetable classification and in recognizing fruit disease problems. We surveyed image-processing approaches used for fruit disease detection, segmentation and classification. We also compared the performance of state-of-the-art methods under two scenarios, i.e., fruit and vegetable classification and fruit disease classification. The methods surveyed in this paper are able to distinguish among different kinds of fruits and their diseases that are very alike in color and texture.

Apple disease classification using color, texture and shape features from images

The presence of diseases in several kinds of fruits is the major factor of production and the economic degradation of the agricultural industry worldwide. An approach for the apple disease classification using color-, texture- and shape-based features is investigated and experimentally verified in this paper. The primary steps of the introduced image processing-based method are as follows: (1) infected fruit part detection is done with the help of K-means clustering method, (2) color-, texture- and shape-based features are computed over the segmented image and combined to form the single descriptor, and (3) multi-class support vector machine is used to classify the apples into one of the infected or healthy categories. Apple fruit is taken as the test case in this study with three categories of diseases, namely blotch, rot and scab as well as healthy apples. The experimentation points out that the introduced method is better as compared to the individual features. It also points out that shape feature is not better suited for this purpose.