Identification Of Food Grains And Its Quality Using Pattern Classification

The research work deals with an approach to perform texture and morphological based retrieval on a corpus of food grain images. The work has been carried out using Image Warping and Image analysis approach. The method has been employed to normalize food grain images and hence eliminating the effects of orientation using image warping technique with proper scaling. The images have been properly enhanced to reduce noise and blurring in image. Finally image has segmented applying proper segmentation methods so that edges may be detected effectively and thus rectification of the image has been done. The approach has been tested on sufficient number of food grain images of rice based on intensity, position and orientation. A digital image analysis algorithm based on color, morphological and textural features was developed to identify the six varieties rice seeds which are widely planted in Chhattisgarh region. Nine color and nine morphological and textural features were used for discriminant analysis. A back propagation neural network-based classifier was developed to identify the unknown grain types. The color and textural features were presented to the neural network for training purposes. The trained network was then used to identify the unknown grain types

STUDY AND ANALYSIS OF STATISTICAL FEATURES OF FACE EXPRESSION IN NOISY ENVIRONMENT

This paper presents a new approach for the recognition of emotions in noisy environment. The approach presents the cascading of Wiener filter and Mutation based bacteria optimization technique (MBFO) to remove the noise from the highly corrupted face image .After removing the noise by the combination of wiener filter and MBFO technique and then detects the local , global and statistical feature form the image. Bacterial foraging optimization algorithm (BFOA) is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains. In this research paper seven emotions namely anger, fear, happiness, surprise, sad, disgusting and neutral will be tested from database in noisy environment of speckle noise. facial expressions recognition system is based on a representation of the expression, learned from a training set of pre-selected meaningful features. However, in reality the noises that may embed into an image document will affect the performance of face recognition algorithms. Finally, emotion recognition will be performed by giving the extracted eye, lip and mouth blocks as inputs to a feed-forward neural network trained by back-propagation

Real-time search-free multiple license plate recognition via likelihood estimation of saliency

In this paper, we propose a novel search-free localization method based on 3-D Bayesian saliency estimation. This method uses a new 3-D object tracking algorithm which includes: object detection, shadow detection and removal, and object recognition based on Bayesian methods. The algorithm is tested over three image datasets with different levels of complexities, and the results are compared with those of benchmark methods in terms of speed and accuracy. Unlike most search-based license-plate extraction methods, our proposed 3-D Bayesian saliency algorithm has lower execution time (less than 60 ms), more accuracy, and it is a search-free algorithm which works in noisy backgrounds

Performance Analysis of Image Enhancement Techniques for Brain Tumor Images

Abstract- The reconstruction of the medical images of the brain tumor is very helpful for the radiologist to diagnose the tumor efficiently, other biological research and surgical planning. The research paper is about the analysis of performance of the various image enhancement techniques for MRI of brain tumor. This research finds the best enhancement technique out of Weiner filter, Blind convolution method and Median filter so that tumor can be detected properly. In the process, the image is acquired from the external device than the preprocessing of the MR image and various image enhancement techniques are applied to obtain an image with improved quality which may give best results on getting diagnosed. The best technique is chosen based on peak signal to noise ratio (PSNR) factor. The filter which gives highest PSNR value is chosen as the best filter for enhancement. Index Terms- 3D medical image enhancement; brain tumor detection; enhancement techniques. 1

Towards multi-class detection:a self-learning approach to reduce inter-class noise from training dataset

This paper proposes a novel self-learning framework, which converts a noisy, pre-labeled multi-class object dataset into a purified multi-class object dataset with object bounding-box annotations, by iteratively removing noise samples from the low-quality dataset, which may contain a high level of inter-class noise samples. The framework iteratively purifies the noisy training datasets for each class and updates the classification model for multiple classes. The procedure starts with a generic single-class object model which changes to a multi-class model in an iterative procedure of which the F-1 score is evaluated to reach a sufficiently high score. The proposed framework is based on learning the used models with CNNs. As a result, we obtain a purified multi-class dataset and as a spin-off, the updated multi-class object model. The proposed framework is evaluated on maritime surveillance, where vessels need to be classified into eight different types. The experimental results on the evaluation dataset show that the proposed framework improves the F-1 score approximately by 5% and 25% at the end of the third iteration, while the initial training datasets contain 40% and 60% inter-class noise samples (erroneously classified labels of vessels and without annotations), respectively. Additionally, the recall rate increases nearly by 38% (for the more challenging 60% inter-class noise case), while the mean Average Precision (mAP) rate remains stable.</p