The image content detectability and image structure preservation are closely related concepts with undeniable role in image quality assessment. However, the most attention of image quality studies has been paid to image structure evaluation, few of them focused on image content detectability. Examining the image structure was firstly introduced and assessed in Structural SIMilarity (SSIM) measure, in which, the definition of image structure is constrained to the intensity covariance between the reference and test images. Indeed, this measure discerns the luminance changes in the pixels of the reference and test images, by employing the low-level statistical features. But this minimal definition of image structure does not cover the issue of image content detectability. In this study, we found that the status of image region smoothness can reflect its structural content. So, we proposed a novel smoothness measure based on the Maximally Stable Extremal Regions (MSER) descriptor. Subsequently, we proposed a novel image structural similarity measure, in which the fidelity of image region smoothness is also taken into account. Experimental results on five popular benchmark image databases, including A57, LIVE, CSIQ, TID2008 and TID2013, are provided, which confirm that the proposed approach has a reasonable prediction performance compared to the state-of-the-art image quality metrics.

Wide-baseline image matching with significant viewpoint differences plays a fundamental role in many computer vision and photogrammetry applications, such as 3D reconstruction and image registration. One of the main problems of matching these images is the existence of a relatively large number of mismatches. Generally, a geometric consistency check process based on various geometrical constraints and robust estimator methods such as the epipolar line and RANSAC algorithm is used for mismatch elimination. However, conventional geometry fi ltering methods in wide-baseline images will fail if the number of outliers is very high. In addition, these methods have high computational complexity. In this paper, a novel mismatch elimination approach in wide-baseline images with significant viewpoint differences is presented. First, initial elliptical features are extracted using improved MSER (Maximally Stable Extremal Regions) detector in both images. Then, a distinctive DAISY descriptor is generated for each extracted feature. In the next step, the initial feature correspondence process is established using Euclidean distance between feature descriptors. Then, a novel mismatch elimination approach based on features shape matrix, named MESM (mismatch elimination based on shape matrix), is applied. Finally, the few remained blunders are removed by using a geometric constraint. The proposed image matching and mismatch elimination algorithms were successfully applied to match eight close-range image pairs with significant viewpoint differences, and the results demonstrate its capability to improve matching performance.

Nowadays, due to quality development of satellite images, automatic target detection on these images has been attracted many researchers' attention. Remote-sensing images follow various geospatial targets; these targets are generally man-made and have a distinctive structure from their surrounding areas. Different methods have been developed for automatic target detection. In most of these methods, target searching was used for features extraction or matching in order to detect the geospatial targets. Hence, in this paper, in order to improve computational time in target detection process, the areas of images with high probability of geospatial target existence, were selected. This will significantly improve the automation level of the process and computational time in following processes. For this purpose, a combination of saliency models and the of-local features detector's algorithm were used. The proposed method consists of three main steps, including local feature extraction by applying MSER algorithm, saliency maps generation by applying AWS and WMAP models and Regions determination with high probability of geospatial target existence. In this paper, a threshold was defined by calculating the saliency values in the whole image. Salient Regions were detected by applying the threshold for each extracted area. This method was implemented on six satellite images by different sensors and the mentioned six satellite images derived from Google Earth software that consist several geospatial targets with various backgrounds. Two criteria were used in order to quantitatively assess the results. Moreover, this method was compared with the mean shift segmentation algorithm in the color space images that was applied to detect saliency Regions of an image. The results of the proposed method showed that the average of the detected areas was 5. 1% of the total area of the images in which 98. 28% of the targets were located in these Regions. In addition, the average amount of computational time was 22. 1 seconds. The results showed the superiority of the proposed method in terms of accuracy and computational speed.

The Fourier‎ -‎Br\'os‎ -‎Iagolnitzer (FBI) transform is the right tool to characterize microlocal analyticity‎, ‎microlocal smoothness‎, ‎and Gevrey regularity‎. ‎In this paper‎, ‎we characterize microlocal Gevrey regularity of a distribution on a Maximally real submanifold of $\mathbb{C}^m$ using the FBI transform‎.

One of the most commonly occurring problems during machining is Machine tool chatter, which adversely affects surface finish, dimensional accuracy, tool life and machine life. Machine tool chatter can be modeled as a linear time invariant differential equation with time delay or delay differential equation. Infinite dimensional nature of delay differential equations is apparent in the study of time delay systems. The analytical stability methods are thus more difficult for these differential equations and approximate methods do not give accurate results. In this paper, a new method is developed to determine the exact Stable region(s) in the parameter space of machine tool chatter. In this method, first, the bifurcation points are determined. Then, the Lambert function is used to decide on the stability characteristics of each particular region. The advantages of this method are simple implementation and applicability to high order linear time delay systems. From the resulting stability Regions of this method, an optimal spindle speed can be chosen to suppress the chatter. The new approach is the most acceptable method in comparison to traditional graphical, computational and approximate methods due to excellent accuracy and other advantages.

BACKGROUND AND OBJECTIVE: Nowadays, besides the ordinary staining, a variety of cell markers and immunohistochemistry methods are incorporated for distinction between neoplasms. But in the current situation, due to the high cost of most of these methods, cheaper and simpler way like silver nitrate staining should be sought for analyzing the nucleolar organizer dots. Therefore, this study was conducted to evaluate the distinction capability of silver nitrate within the benign and malignant neoplasms of the salivary glands. METHODS: Silver nitrate staining was carried out for nucleolar organizer Regions in 22 cases of the most common benign neoplasms of the salivary glands, namely pleomorphic adenoma, and 22 cases of common malignant neoplasms of salivary glands, consisting of 11 adenoid cystic carcinoma and 11 mucoepidermoid carcinoma cases. Twenty-two cases of normal salivary glands of the peripheral Regions of the same samples were analyzed. FINDINGS: The mean dots occurrences for normal salivary glands, pleomorphic adenoma, adenoid cystic carcinoma and mucoepidermoid carcinoma were 1.67±0.2, 2.32±0.39, 3.92±1.11 and 3.25±0.78 (for malignancies was 3.58±1), respectively.  There were significant differences among benign and malignant, malignant and normal, and benign and normal groups (p<0.05). CONCLUSION: This study purposed that the use of silver nitrate staining method is suitable for distinction of the benign and malignant neoplasms of salivary glands.

The vertex PI index PI (G) = SxyÎE (G) [nxy (x) + nxy (y)] is a distance-based molecular structure descriptor, where nxy (x) denotes the number of vertices which are closer to the vertex x than to the vertex y and which has been the considerable research in computational chemistry dating back to Harold Wiener in 1947. A connected graph is a cactus if any two of its cycles have at most one common vertex. In this paper, we completely determine the Extremal graphs with the greatest and smallest vertex PI indices mong all cacti with a fixed number of vertices. As a consequence, we obtain the sharp bounds with corresponding Extremal cacti and extend a known result.