Image mosaicing refers to stitching two or more Images which have overlapping regions to a larger and more comprehensive Image. Scale Invariant Feature transform (SIFT) is one of the most commonly used detectors previously used in Image mosaicing. The defects of SIFT algorithm are the large number of redundant keypoints and high execution time due to the high dimensions of classical SIFT descriptor, that reduces the efficiency of this algorithm. In this paper, to solve these problems a new four-step approach for Image mosaicing is proposed. At first, the keypoints of both reference and sensed Images are extracted based on Redundant Keypoint Elimination-SIFT (RKEM-SIFT) algorithm to improve the mosaicing process. Then, to increase the speed of the algorithm, the 64-D SIFT descriptor for keypoints description is used. Afterwards, the proposed RANdom SAmple Consensus (RANSAC) algorithm is used for removing mismatches. Finally, a new method for Image blending is proposed. The details of the proposed steps are as follows. RKEM-SIFT algorithm has been proposed in [1] to eliminate redundant points based on redundancy index. In this paper, RKEM algorithm is used to extract keypoints to improve the accuracy of Image mosaicing. In the second stage, for each keypoint of the Image, 64-D SIFT descriptor is computed. In this descriptor, unlike the 128-D SIFT descriptor, a smaller window is used which improves the accuracy of matching and reduces the running time. In the third stage, the proposed adaptive RANSAC algorithm is suggested to determine the adaptive threshold in the RANSAC algorithm to remove the mismatches and to improve the Image mosaicing. Determining the appropriate threshold value in RANSAC is so important, because if an appropriate value is not chosen for this algorithm, the mismatches are not removed, and eventually there will be a serious impact on the outcome of the Image mosaicing process. In this method, the threshold value is based on the median value of distances between matching points and their transformed model. Image blending in the mosaicing process is the final step which blends the pixels intensity in the overlapped region to avoid seams. The proposed method of blending is to combine the Images based on the Gaussian weighting function, which the mean of this function is considered as the average of the data in the overlapped region of two Images. The proposed blending method reduces artifacts in the Image for better performance of the mosaicing process. Another advantage of this proposed method is the possibility to combine more than two Images that are suitable for creating panoramic Images. The simulation results of the proposed Image mosaicing technique, which includes the RKEM-SIFT algorithm as feature detector, 64-D SIFT descriptor, proposed adaptive RANSAC algorithm and proposed Image blending algorithm on different Image databases show the superiority of the proposed method according to RMSE criteria, precision and running time.

In this paper, an alternative approach in operational modal analysis is presented, utilizing Image processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

Purpose: The study purpose was to determine and compare the Recall and Precision of Bing and Google Image search engines for content based Image retrieval. Methodology: The research used webometrics and comparative methods. Population includes Images stored in the databases of Bing and Google search engines, and research sample includes 15 selective Images searched in any of search engines. All the retrieved sources through the Images by Image content based Image search were gathered, results’ Recall and Precision measures were calculated by relevance formula and their average percentage were obtained. Research hypotheses were tested by U Mann-Whitney test as well. Findings: Findings showed that the Google search engine functionality was higher with recall measure of % 88. 73 than recall rate (%20. 86) for Bing. But Bing search engine had higher precision (% 99. 86) than Google (%94. 80). Results: Hypotheses tests on recall and precision in two search engines’ Image retrieval showed a significant difference for recall in favor of Google, indicating its better functionality than Bing but there was no significant difference between them concerning precision since both showed fair precision however Bing was relatively useful.

In Kant and the Problem of Metaphysics (KPM), Heidegger shows how the initial Image-making power of the imagination is pictorial in the pure Image of time. He further clarifies the recognized link between the syntheses and the time-visualizing power of Images which is pictorial formative power, which can visualize time. The three syntheses of imagination are replaced with the modes of existence of Dasein. Three kinds of photographs resemble the Images produced when the transcendental imagination functions as Dasein. We can address the ways to visualize time by considering Dasein with a photographic preoccupation through 1. A possible look in the form of presentation (a photograph shows an immediate look of a house or person as intuitable this-here); 2. An immediate look of the thing, house, or deceased person, as well as a copy or its reproduction as a death mask; and, 3. The manner of contemplation of a likeness is an immediate look of a being present in the form of representation.

Particle-size and their distribution are important in various sciences such as: agricultural, natural resources, and even engineering and medical engineering. Using mechanical sieves requires considerable cost and time. The purpose of this study was to investigate the capability of Image processing techniques for desert pavement. Thus, random sampling of desert pavement was carried out in bare and epandage pediments. Samples were transferred to the laboratory and was grading using standard sieve series. The collected samples taken were also photographed. Photos were analyzed using Image Processing and Analysis in Java and Geological Image Analysis Softwares. Then the results of the software were compared with the values measured by the sieve series. Results, show that the values obtained for particle aggregation using Image j software have a correlation of 93% in the epandage Pediment particles and 96% in the bare Pediments with mechanical sieve. Results of correlation between mechanical sieving and GIAS software in epandage pediment have an average correlation of 91% and in bare pediment has a correlation of 96%. It can be concluded that the use of these softwares can be a good substitute for mechanical sieve in particle size distribution. Using the results of this study can be effective in reducing cost and time in perform grading.

This paper presents a novel method for cooperative educational dissemination systems. Taking into consideration the inherent characteristics of distance learning video streams (existence of a few slow moving objects in a classroom), we have proposed a novel content-based video coding method that is very efficient at low bitrate channels. On the encoding side, we have applied a background subtraction algorithm for motion segmentation using a novel statistical background modeling approach. At each frame, the moving objects are extrapolated with a rectangular model and tracked frame by frame (which forms the only data needed to be sent over the channel). On the decoding side, we have used a new error concealment algorithm (based on edge information of frames) to eliminate probable channel errors in the received data. Moreover, a new fuzzy scene modeling algorithm is proposed that adaptively computes the alpha blending coefficient (used in dynamic video mosaicing) and reconstructs the original video scene from partially overlapped frames. Our experiments show that the proposed coding system is very efficient in real-time video webcasting with approximately 24 fps for CIF formatted sequences (and at a minimum of 13 fps transmission for 720 x 576 frame sizes). Applying our proposed system has reduced the required bitrate of H.264 and MPEG-4 coding standards by about 2.5% to 8%, respectively, with almost the same, or even better, reconstructed video qualities.

Introduction: Rice (Oryza sativa L.) feeds more than half of the world's population. Rice is considered a model plant in biological and agriculture field research due to its small genome compared with other plants. Storage proteins in the aleurone layer of rice seeds include albumin and globulin, as well as the endosperm, prolamin, and glutelin proteins. To determine the number of proteins and identify the protein spots between the tissues of two embryo samples and rice endosperm, the protein spots were matched according to their expression level in the Images obtained from two-dimensional gel electrophoresis. Materials and methods: The profile of proteins in the endosperm and embryo of rice seeds was determined using isoelectric focusing (IEF) and polyacrylamide gel electrophoresis(SDS-PAGE) methods. The Image Master software was utilized to analyze the two-dimensional gels. Considering the importance of local rice consumption in Iran and the significant production of this economically important crop in the country, this study aimed to determine the diversity and differences in the expression of protein spots within the linear pH range of 4 to 7. The two-dimensional electrophoresis method was employed on the Hasan-Sarai variety of rice indigenous to the region of Guilan. For this research, the cultivar being examined was procured from the Guilan Rice Research Center. Subsequent stages of the experiment, including sample separation and total protein extraction, were conducted at the Payame Noor University laboratory in Asadabad. Ultimately, the protein samples obtained were sent to the Health Research Institute at the Faculty of Pharmacy, where two-dimensional electrophoresis was carried out using an isoelectric focusing device. Finally, the extracted protein samples were transferred to the Health Research Institute of the Faculty of Pharmacy to perform two-dimensional electrophoresis using an isoelectric focusing device. Results: Based on the results of the two-dimensional gel analysis, a large part of the protein spots are present in the pH range of 4 to 7. The analysis of protein profile based on standard proteins with specific molecular weight and isoelectric point indicates that embryo tissue and endosperm based on isoelectric point and molecular weight showed that 284 protein spots in the embryo and 123 protein spots in endosperm have been identified. Among the identified spots, 57 common protein spots showed increased and decreased expression between the two samples. The presence of remaining protein spots in the two samples showed a significant difference between protein content in the embryo and endosperm. Conclusion: Image master software was used to determine the isoelectric point and molecular weight of protein spots and expression changes between common spots. Analysis was done in protein databases and possible proteins were identified in embryo tissue and endosperm. The proteins identified in rice endosperm and embryo tissue in two-dimensional electrophoresis included grain storage proteins, glutelin precursors, acidic and alkaline glutelins, globulins, prolamins, HSPs, and hypothetical proteins. Therefore, in this way, it is possible to identify the level of expression of proteins and also their biosynthetic pathways.