histogram equalization

AbstractFacial expression recognition is one of the most important computer vision issues that has many applications. One of them is the Human computer interaction. In this paper, a method for facial expression recognition using texture and edge descriptors is proposed. Facial expression recognition generally consists of three steps: preprocessing, feature extraction and classification. In this paper, histogram Equalization has been used in the proposed method for pre-process the input images in which the face is present. In this paper, the focus is on the feature extraction and a combination of LDP1 and HOG2 descriptors has been used to improve the existing methods. After feature extraction, the support vector machine was used to classification the facial expression recognition. This article uses the JAFFE database. The database contains 213 images of seven facial expressions (happy, sad, angry, fear, disgust, surprised and natural) taken from 10 Japanese female models. The results showed that the proposed method with 99.04% accuracy in the facial recognition test had a better performance than the methods of previous researchers.

In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. Local methods suggested in recent years, have obtained better results than global methods. However by more intelligent training in such a way that first, important data is more effective for training, second, clustering in such way that training blocks lie in low-rank subspaces, we can design a dictionary applicable for image de-noising and obtain results near the state of the art local methods. In the present paper, we suggest a method based on global clustering of image constructing blocks. As the type of clustering plays an important role in clustering-based de-noising methods, we address two questions about the clustering. The first, which parts of the data should be considered for clustering? and the second, what data clustering method is suitable for de-noising.? Then clustering is exploited to learn an over complete dictionary. By obtaining sparse decomposition of the noisy image blocks in terms of the dictionary atoms, the de-noised version is achieved. In addition to our framework, 7 popular dictionary learning methods are simulated and compared. The results are compared based on two major factors: (1) de-noising performance and (2) execution time. Experimental results show that our dictionary learning framework outperforms its competitors in terms of both factors.