Texture features play an important role in most image retrieval techniques to obtain results of high accuracy. In this work, the face image retrieval method considering texture analysis and statistical features has been proposed. Textile features can also be extracted using the GLCM tool. In this research, the GLCM calculation method involves two phases, first: some of the previous image processing techniques work together to get the best results to determine the big object of the face image (center of face image) then, the gray level cooccurrence matrix GLCM is computed for gray face image and then some statistical texture features with second order are extracted. The second phase, the facial texture features are retrieved by finding the minimum distance between texture features of an unknown face image with the texture features of face images which are stored in the database system. The experimental results show that the proposed method is capable to achieve high accuracy degree in face image retrieval.

The detection of diseases affecting plant is very important as it relates to the issue of food security, which is a very serious threat to human life. The system of diagnosis of diseases involves a series of steps starting with the acquisition of images through the pre-processing, segmentation and then features extraction that is our subject finally the process of classification. Features extraction is a very important process in any diagnostic system where we can compare this stage to the spine in this type of system. It is known that the reason behind this great importance of this stage is that the process of extracting features greatly affects the work and accuracy of classification. Proper selection of the right features leads to high accuracy in the system diagnostics and vice versa. The proposed system collect images of different crop (Rice, cotton and tomato) disease, we will enter the images of cropping them , then Re-size the images to fixed size, then improve the image through Fuzzy histogram equalization (FHE) , then perform image segmentation  using color based K-means  and finally compare the methods of features  extraction (Percentage of Leaf Area Infected (PI),Texture-Based Features, Color Moments, Features obtained by Color Co-occurrence Method and Shape based Features) we found that the use of 4 methods together (Percentage of Leaf Area Infected (PI),Texture-Based Features, Color Moments and Shape based Features) produce excellent result..

Strabismus is one of the widespread vision disorders in which the eyes are misaligned and asymmetric. Convolutional neural networks (CNNs) are properly designed for analyzing images and detecting texture patterns. In this paper, we proposed a system that uses deep learning CNN applications for automatically detecting and classifying strabismus disorder. The proposed system includes two main stages: first, the detection of facial eye segmentation using the viola-jones algorithm. The second stage is to map the segmented eye area according to the iris position of each eye. This method is applied to three strabismus datasets, gathered as digital images. The second section covers the segmentation of the eye region. Besides, the evaluation equations for measuring system performance. The system has undergone numerous experiments in various stages to simulate and analyze the detection performance of CNN layers through different classifiers and variant thresholds ratio. The researchers investigated the experimental outcomes during the training and testing phases and obtained promising results that exhibit the effectiveness of the proposed system. According to the results, the accuracy of this technique reached 95.62%.

Principle component analysis produced reduction in dimension, therefore in our proposed method used PCA in image lossy compression and obtains the quality performance of reconstructed image. PSNR values increase when the number of PCA components is increased and CR, MSE, and other error parameters decreases when the number of components is increased.

Image processing are the important source of data and information in all fields, many types of Fruit and vegetable can be found in supermarket. When a cashier cannot scan the barcode, cannot recognize the fruit and vegetable that a customer wants. Software is needed to ease the process of vegetable recognition. The purpose of this paper is to simulate and design software that can be used to recognize different types of fruit and vegetable based on its shape and classification using neural networks. The system begins by collecting different types of both. Images are capture  in fixed amount  of light, background and other effects using a mobile camera (13 mega pixels) ,convert input image Gray; After the conversion process calculate the different  intensity value, where the density value different of the object from the background so we determine the value of the threshold of separation between them. With the help of the threshold value convert gray image to binary   image and used edges detect to determine the shape. Followed by training Neural Networks (NN)  To recognition and classification For items, the accuracy system is 95 percent.  Done using MATLAB 2017Ra software.

The detection of diseases affecting wheat is very important as it relates to the issue of food security, which poses a serious threat to human life. Recently, farmers have heavily relied on modern systems and techniques for the control of the vast agricultural areas. Computer vision and data processing play a key role in detecting diseases that affect plants, depending on the images of their leaves. In this article, Fuzzy- logic based Histogram Equalization (FHE) is proposed to enhance the contrast of images. The fuzzy histogram is applied to divide the histograms into two subparts of histograms, based on the average value of the original image, then equalize them freely and independently to conserve the brightness of the image. The proposed method was evaluated using two well-known parameters: Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The best results were reflected by MSE = 0.071 and PSNR =39.58 for the Mildew Powdery disease. It is impressive to recognize that the proposed method yielded clear positive outcomes through producing better contrast enhancement while preserving the details of the original image, as confirmed by the subjective metrics.

Control of diseases affecting plants is very important as it relates to the issue of food security, which is a very serious threat on human life. According to the International Maize and Wheat Improvement Center (CIMMYT), most of the maize diseases are caused by mildew. There are more than fifty different mildew diseases affecting maize. The diseases that infect plants go through different stages of the degree of infection. Therefore, determining the degree of injury helps decision-makers spend money on controlling the disease properly. The common method used to determine the degree of injury is visual examination. It’s known that visual examination leaves a large area for error. It is unable to determine the degree of injury with high accuracy as a percentage for example. In this study, we used two methods of segmentation to determine the injury ratio in maize leaves. The methodology of segmentation depends on the Color Threshold Application (CTA) and K-mean clustering. For comparison between methods of segmentation, different color spaces (RGB, HSV, YCbCr, and L*a*b*) have been used. The results obtained indicate that the color threshold segmentation is more efficient than K-means clustering in terms of determining the injury ratio, especially with HSV color space. While it contrary to the RGB color model because it gives the worst result.

Abstract:The Prunus_armeniaca fruit is classified manually in wholesale markets, supermarkets and food processing plants on a normal or defects basis. The aim of this research is to replace the manual sorting techniques using computer vision techniques and applications by proposing techniques for identify and recognitions patterns through the use of 150 fruits of Prunus_armeniaca, 10 for the testing stage in fresh and 10 for testing stage in case of defects. The fruits Prunus_armeniaca collected from growing trees in the large fields of Salah al-Din province\Iraq. The system designed for classification based on the color image taken inside a black box used camera pixel resolution of (13 mega) with a constant intensity of light. . Used K-mean in phase segmentations and only computed 13 features derive statistics from GLCM .classification phase used SVM classify fruit into two class, either (normal or defects) .Results the system success rate reach 100%.The work done using MATLAB R2016a.