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.

Content-based image retrieval is one of the interesting subjects in image processing and machine vision. In image retrieval systems, the query image is compared with images in the database to retrieve images containing similar content. Image comparison is done using features extracted from the query and database images. In this paper, the features are extracted based on the human visual system. Since the human visual system considers the texture and the Edge Orientation in images for comparison, the colour Difference Histogram associated with the image’ s texture and Edge Orientation is extracted as a feature. In this paper, the features are selected using the Shannon entropy criterion. The proposed method is tested using the Corel-5K and Corel-10K databases. The precision and recall criteria were used to evaluate the proposed system. The experimental results show the ability of the proposed system for more accurate retrieval rather than recently content-based image retrieval systems.

In this paper, a new algorithm for Edge detection based on fuzzy concept is suggested. The proposed approach defines dynamic membership functions for different groups of pixels in a 3 by 3 neighborhood of the central pixel. Then, fuzzy distance and -cut theory are applied to detect the Edge map by following a simple heuristic thresholding rule to produce a thin Edge image. A large number of experiments are employed to con firm the robustness of the proposed algorithm. In the experiments different cases such as normal images, images corrupted by Gaussian noise, and uneven lightening images are involved. The results obtained are compared with some famous algorithms such as Canny and Sobel operators, a competitive fuzzy Edge detector, and a statistical based Edge detector. The visual and quantitative comparisons show the effectiveness of the proposed algorithm even for those images that were corrupted by strong noise.

For a Coloring c of a graph G, the Edge-di erence Coloring sum and Edge-sum Coloring sum with respect to the Coloring c are respectively Σ c D(G) = Σ jc(a) 􀀀 c(b)j and Σ s S(G) = Σ (c(a) + c(b)), where the summations are taken over all Edges ab 2 E(G). The Edge-di erence chromatic sum, denoted by Σ D(G), and the Edge-sum chromatic sum, denoted by Σ S(G), are respectively the minimum possible values of Σ c D(G) and Σ c S(G), where the minimums are taken over all proper Coloring of c. In this work, we study the Edge-di erence chromatic sum and the Edge-sum chromatic sum of graphs. In this regard, we present some necessary conditions for the existence of homomorphism between two graphs. Moreover, some upper and lower bounds for these parameters in terms of the fractional chromatic number are introduced as well.

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.

Due to the progress of the digital image world and increasing numbers, preparing a system for image retrieval is essential. A content-based image retrieval system should find similar images to the image search by a user. In this paper, a novel content-based image retrieval system is proposed. Considering the importance of texture in an image, we introduce a new feature as the Histogram of the texture Difference in the equal Edge Orientation. Then, the expressed features are extracted from training images in the proposed system. Then these features are learned using a support vector machine. The proposed system is examined using the standard WANG database. The results show the efficiency of the proposed system in retrieving images compared to similar methods.

Introduction: Content Based Image Retrieval (CBIR) is a method of image searching and retrieval in a database. In medical applications, CBIR is a tool used by physicians to compare the previous and current medical images associated with patients pathological conditions. As the volume of pictorial information stored in medical image databases is in progress, efficient image indexing and retrieval is increasingly becoming a necessity.Materials and Methods: This paper presents a new content based radiographic image retrieval approach based on Histogram of pattern Orientations, namely pattern Orientation Histogram (POH). POH represents the spatial distribution of five different pattern Orientations: vertical, horizontal, diagonal down/left, diagonal down/right and non-Orientation. In this method, a given image is first divided into image-blocks and the frequency of each type of pattern is determined in each image-block. Then, local pattern Histograms for each of these image-blocks are computed.Results: The method was compared to two well known texture-based image retrieval methods: Tamura and Edge Histogram Descriptors (EHD) in MPEG-7 standard. Experimental results based on 10000 IRMA radiography image dataset, demonstrate that POH provides better precision and recall rates compared to Tamura and EHD. For some images, the recall and precision rates obtained by POH are, respectively, 48% and 18% better than the best of the two above mentioned methods.Discussion and Conclusion: Since we exploit the absolute location of the pattern in the image as well as its global composition, the proposed matching method can retrieve semantically similar medical images.

Localizing text regions in images taken from natural scenes is one of the challenging problems due to variations in font, size, Color and Orientation of text. In this paper, we introduce a new concept so called Edge Color Signature for localizing text regions in an image. This method is able to localize both Farsi and English texts. In the proposed method first a pyramid using different scales of the input image is created. Then for each level of the pyramid an Edge map is extracted. Afterward, several geometric features are employed to filter out the non-text Edges from the extracted Edges. At this stage we describe an Edge using Colors of its neighboring pixels. We use the mean-Shift algorithm to obtain the Color modes surrounding each Edge pixel. Subsequently, the connected Edge pixels with similar Color signatures are clustered using Single-Linkage clustering algorithm to construct meaningful groups. Finally, each of the clusters is labeled as text or non-text using an MLP based cascade classifier. The proposed method has been evaluated on well-known ICDAR 2013 and our Farsi dataset, the result is very promising.