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This paper concentrates on designing an object recognition algorithm utilizing image segmentation. The main innovations of this paper lie in that we convert the image segmentation problem into graph cut problem, and then the graph cut results can be obtained by calculating the probability of intensity for a given pixel which is belonged to the object and the background intensity. After the graph cut process, the pixels in a same component are similar, and the pixels in different components are dissimilar. To detect the objects in the test image, the visual similarity between the segments of the testing images and the object types deduced from the training images is estimated. Finally, a series of experiments are conducted to make performance evaluation. Experimental results illustrate that compared with existing methods, the proposed scheme can effectively detect the salient objects. Particularly, we testify that, in our scheme, the precision of object recognition is proportional to image segmentation accuracy. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of massive medical images retrieval, against the defects of the single-node medical image retrieval system, a massive medical images retrieval system based on Hadoop is put forward. Brushlet transform and Local binary patterns algorithm are introduced firstly to extract characteristics of the medical example image, and store the image feature library in the HDFS. Then using the Map to match the example image features with the features in the feature library, while the Reduce to receive the calculation results of each Map task and ranking the results according to the size of the similarity. At the end, find the optimal retrieval results of the medical images according to the ranking results. The experimental results show that compared with other medical image retrieval systems, the Hadoop based medical image retrieval system can reduce the time of image storage and retrieval, and improve the image retrieval speed. [ABSTRACT FROM AUTHOR]

Based on dark channel priority, the paper proposes an improved defogging algorithm of single image which can defog the foggy images rapidly. The algorithm in the paper applies the method combining adaptive median filter and bilateral filter to figure out clear dark channel on the edge. And the algorithm is based on the physical model of foggy images to estimate transmission. Compared with the traditional algorithm, the estimated transmission is detailed and clear, and has no need to be optimized, which not only overcomes the disadvantages of traditional algorithm using plenty of time to optimize transmission, but also reduces the complexity of the algorithm. The experimental results indicate that the algorithm realizes rapid and high-quality defogging on single image. [ABSTRACT FROM AUTHOR]

Content-Based Image Retrieval (CBIR) is one of the most active hot spots in the current research field of multimedia retrieval. According to the description and extraction of visual content (feature) of the image, CBIR aims to find images that contain specified content (feature) in the image database. In this paper, several key technologies of CBIR, e. g. the extraction of the color and texture features of the image, as well as the similarity measures are investigated. On the basis of the theoretical research, an image retrieval system based on color and texture features is designed. In this system, the Weighted Color Feature based on HSV space is adopted as a color feature vector, four features of the Co-occurrence Matrix, saying Energy, Entropy, Inertia Quadrature and Correlation, are used to construct texture vectors, and the Euclidean distance for similarity measure is employed as well. Experimental results show that this CBIR system is efficient in image retrieval. [ABSTRACT FROM AUTHOR]

This paper presents a powerful video surveillance system that can automatically detect a flame after the target has been captured by the camera. Due to the increased number of web-cameras being installed in many locations, the flame, with its own visible characteristics has a lot of potential research interests in safety protection services. When a flame scenario occurs, the corresponding input video frame will be marked by a rectangle which covers the flame area after being analyzed by the system. Further to this research the detection algorithms and methods will be more robust and this system allows for the classification of the type of fuel that is burning. Our research contributions are towards the development of an almost real-time flame detection system to aid with fire safety precautions in all kinds of environments with appropriate storage of these fire scenario pictures in a fixed file for further exploration of these flame features. [ABSTRACT FROM AUTHOR]