Face recognition is a challenging problem due to different illuminations, poses, facial expressions, and occlusions. In this paper, a new robust face recognition method is proposed based on the Color and edge orientation difference histogram. Firstly, the Color and edge orientation difference histogram is extracted using Color, Color difference, edge orientation, and edge orientation difference of the face image. Then the backward feature selection is employed in order to reduce the number of features. Finally, the Canberra measure is used to assess the similarity between the images. The Color and edge orientation difference histogram shows the Color and edge orientation difference between two neighboring pixels. This histogram is effective for face recognition due to the different skin Colors and different edge orientations of the face image, which leads to a different light reflection. The proposed method is evaluated on the Yale and ORL face datasets. These datasets consist of gray-scale face images under different illuminations, poses, facial expressions, and occlusions. The recognition rate over the Yale and ORL datasets is achieved to be 100% and 98. 75%, respectively. The experimental results demonstrate that the proposed method outperforms the existing methods in face recognition.

In digital Color imaging, it is of interest to transform the Color scene of an image to the other. Some attempts have been done in this case using, for example, lab Color space, principal component analysis and recently histogram rescaling method. In this research, a novel method is proposed based on the Resenfeld and Kak histogram matching algorithm. It is suggested that to transform the Color scene between two images, the histograms of the three R, G and B channels of the input image would be matched to the corresponding histograms of the destination one. The performance of the introduced method was investigated for several images. The obtained results indicated that this method is well capable of transforming the Color scene between images.

Most Color reduction methods that are based on image clustering in a 3D Color space have extremely high computational costs, especially for large size images. In this paper, a new fast adaptive Color reduction method is proposed which, computationally, is independent of the image size and reduces the pixel depth from 24 bits (used to represent tristimulus values in the most commonly hardware-oriented RGB model) to a maximum of 15 bits. To achieve this purpose, by introducing a new hybrid cost function and using a modified version of the Gravitational Search Algorithm (GSA), an adaptive histogram binning approach has been developed. Although the cube re-quantization accuracy in the histogram binning approach is lower compared to the 3D data clustering method, it leads to a significant reduction in computational cost. In this paper, while taking this advantage, we seek to reduce re-quantization error using the adaptive histogram binning of RGB Color components. Despite a significant reduction in pixel depth, the proposed Color reduction approach, due to the adaptive reduction of image Colors, results in an appropriate Color reduction for a wide variety of images.

Color is an important feature to describe object in visual tracking. Color-based histogram is used to model the object properly and Bhattacharya distance is also used to measure the error between reference and candidate histogram. Particles filter estimate position of target while two-dimension affine transformation is used as state of the system. Considering geometric properties of affine transformation as affine group cause two-dimensional mapping of the object to be closer to the real three-dimensional model. Approximation of optimal importance function of particles filter is obtained from Taylor expansion of Bhattacharya distance. Experiments show the accuracy and stability of the proposed tracker for fast and complex movement of a Color target versus the gray level geometric particle filtering algorithm.

Several histogram equalization methods for enhancing the Color images of Rosa Damascena flowers and some thresholding methods for segmentation of the flowers were examined. Images were taken outdoors at different times of day and light conditions. A factorial experiment in the form of a Completely Randomized Design with two factors of histogram equalization method at 8 levels and thresholding method at 15 levels, was implemented. histogram equalization methods included: CHE, BBHE, BHEPL-D, DQHEPL, DSIHE, RMSHE, RSIHE, and no histogram equalization (NHE) as the control. Thresholding method levels were: Huang, Intermodes, Isodata, Li, maximum entropy, mean, minimum, moments, Otsu, percentile, Renyi‟ s entropy, Shanbhag, Yen, constant, and global basic thresholding method. The effect of these factors on the properties of the segmented images such as the Percentage of Incorrectly Segmented Area (PISA), Percentage of Overlapping Area (POA), Percentage of Undetected Area (PUA), and Percentage of Detected Flowers (PDF) was investigated. Results of histogram equalization analysis showed that DQHEPL and NHE have the statistically significant lowest PUA (11. 13% and 8. 32%, respectively), highest POA (89. 35% and 92. 07%, respectively), and highest PDF (61. 88% and 64. 94%, respectively). Thresholding methods had a significant effect on PISA, PUA, POA, and PDF. The highest PDF belonged to constant, minimum, and Intermodes (75. 07%, 73. 08% and 74. 30%, respectively) They also had the lowest PISA (0. 35%, 1. 29%, and 1. 85%, respectively) and PUA (33. 72%, 23. 09%, and 15. 56%, respectively). These methods had the highest POA (80. 73%, 76. 70%, and 84. 67%, respectively). Hence, they are suitable methods for segmentation of Rosa Damascena flowers in Color images.

The happiness, fear, sadness, anxiety, and etc. feelings appear in the face for a short time after controlling and suppressing. This unmanageable expression is micro-expression. It can show trickery and lie. Therefore, its recognition has a wide of applications in tribunal and therapy. However, it is necessary to identify the rapid and weak movements of the facial muscles. To this end, in this paper, we magnify motions and we have considered six planes along the time dimension to identify changes well. We also propose the multi-Color uniform local binary pattern from six intersection planes and the histogram of gradient direction from these planes, which produce good results in micro-expression recognition. These methods can be used in a real environment. Because they work better in illumination and light changes. The result of the experiments shows 74.12%, 86.19%, and 66.23% accuracy using the proposed method on CASME I, CASME II, and SMIC-NIR databases, respectively. Thus, our method has promising performance in comparison with the methods of previous researches.

Multiple microbes can alter a plant's development and agricultural productivity, which has significant implications for the ecosystem and human life. As a result, timely identification, prevention, and prompt treatment are required. Fundamental methods have some drawbacks to plant disease identification like more time-consuming, accuracy, doesn't support multiple plant detection. This paper introduces a hybrid model that uses a random forest classifier combined with the AdaBoost Classifier to classify plant diseases to overcome the above-said drawbacks. So as to individualize normal and abnormal leaves from data sets, the suggested methodology employs the Random Forest with AdaBoost algorithm. The operational processes in our suggested study are preprocessing, segmentation, feature extraction, training the classifier, and classification. The produced datasets of infected and uninfected leaves are combined and processed using the Random Forest classifier to categorize the infected and uninfected photos. Color histogram is used to gather features from imagery. KNN, Naive Bayes, and SVM are all used to evaluate our suggested technique.