Youngeun An

Localization Algorithm Design and Implementation to Utilization RSSI and AOA of Zigbee

In this paper, we has implemented the localization algorithm through RSSI and AOA of Zigbee. The RSSI (Received Signal Strength Indicator) method is used for the localization of Zigbee and affected a lot by intensity, distance and interruption of signals. The combined algorithm of AOA (Angle of Arrival) and RSSI has been designed and implemented to make up the weaknesses of RSSI. As long as we knew, although the previous system has been implemented and used through the UWB (Ultra Wide-Band) or CSS communication methods, the firstly implemented system is the location recognition algorithm through RSSI and AOA of Zigbee. In this paper has obtained the result from the repeated tests to achieve the measurement of location with the accuracy of average 35 ~ 36 cm when the suggested system was placing the beacon on the four corners of 2 dimensional rectangular space with 4m wide and 2m long, and the receiver on the random space.

CBIR Based on Adaptive Segmentation of HSV Color Space

Proposed algorithm is based on color information using HSV color space. Histogram search characterizes an image by its color distribution, or histogram but the drawback of a global histogram representation is that information about object location, shape, and texture is discarded. Thus local histogram is used for extracting the maximum color occurrence from each segment. Before extracting the maximum color from each segment the input image is adaptively segmented. Different quantization of hue and saturation are used for partitioning the image into different number of segments. Finally minkowski metric is used for feature vector comparison. Web based image retrieval demo system is built to make it easy to test the retrieval performance and to expedite further algorithm investigation

Classification of Feature Set Using K-means Clustering from Histogram Refinement Method

In this paper, we propose to use K-means clustering for the classification of feature set obtained from the histogram refinement method. Histogram refinement provides a set of features for proposed for Content Based Image Retrieval (CBIR). Standard histograms, because of their efficiency and insensitivity to small changes, are widely used for content based image retrieval. But the main disadvantage of histograms is that many images of different appearances can have similar histograms because histograms provide coarse characterization of an image. Hence histogram refinement method further refines the histogram by splitting the pixels in a given bucket into several classes based on color coherence vectors. Several features are calculated for each of the cluster and these features are further classified using the K-means clustering.

Shape from Focus through Laplacian Using 3D Window

One of the fundamental objectives of computer vision is to reconstruct a three-dimensional (3D) structure of objects from two-dimensional (2D) images. The basic idea of image focus is that objects at different distances from a lens are focused at different distances. Shape from Focus (SFF) is the problem of reconstructing the depth of the scene changing actively the optics of the camera until the point of interest is in focus. The point in focus gives information about its depth through the thin lens Gaussian law. An effective focus measure operator should be a high-pass filter. Usually, the variation of frequency components are not enough that focus measure could be computed pixel-wise, therefore, sum of pixels in small 2D windows are used for detecting the high frequency components. In this paper, we propose to use 3D windows instead of 2D windows for detecting the high frequency components in the images. The proposed algorithm using 3D window gives better depth map than the previous algorithms using 2D windows.

An intrinsic semantic framework for recognizing image objects

The paper proposes a new approach to find semantic meanings in visual object class structure, in line with the Gestalt laws of proximity. Micro level semantic structures are formed by line segments (arcs also approximated into line segments based on pixel deviation threshold) which are in close proximity. These structures are hierarchically combined till a semantic label can be assigned. The algorithm extracts semantic groups, their inter-relations and represents these using a graph. Invariant geometrical properties of the groups and relations are used as vertex and edge labels. A graph model captures the inter class variability by analyzing the repetitiveness of structures and relations and uses it as a weighting factor for classification. The algorithm has been tested on a standard benchmark database and compared with existing approaches.

Image Retrieval Using Maximum Frequency of Local Histogram Based Color Correlogram

Color histogram is widely used for image indexing in content-based image retrieval (CBIR). A color histogram describes the global color distribution of an image. It is very easy to compute and is insensitive to small changes in viewing positions. However, the histogram is not robust to large appearance changes. Moreover, the histogram might give similar results for different kinds of images if the distributions of colors are same in the images. On the other hand, color Correlogram is efficiently used for image indexing in content-based image retrieval. Color Correlogram extracts not only the color distribution of pixels in images like color histogram, but also extracts the spatial information of pixels in the images. The characteristic of the color Correlogram to take into account the spatial information as well as the distribution of color pixels greatly attracts the researcher for content based image retrieval. In this paper, we propose the image bin (histogram value divisions) separation technique followed by extracting maxima of frequencies and plotting a Correlogram. At first, the histogram is first calculated for an image. After that, it is subdivided into four equal bins. Each bin is subdivided into four more bins and for every such subdivision the maxima of frequencies s calculated. This information is stored in the form of a Correlogram. The distance between Correlogram of the query image with the corresponding Correlogram of database images is calculated. The proposed algorithm is tested on a database comprising a large number of images.

Web Based Image Retrieval System Using HSI Color Indexes

This paper presents an image retrieval system using HSV color indexes. We classify the image into a fixed number of blocks, extract the key value of each block and assign the index code, which is classified by 24, to the HSV color space. The index code of each image is stored in the database. The desired image is retrieved on the web. Retrieval system outputs the image with a high matching factor according to a distribution chart. A small demonstration system has been tested and shows superior performance compared with the simple color based retrieval system.

Feature extraction through generalization of histogram refinement technique for local region-based object attributes

Content based image retrieval (CBIR) is used to retrieve digital images from large databases. However, the problem of retrieving images on the basis of the contents remains largely unsolved. The proposed method of image retrieval is based on the information provided by histogram analysis of the intensity or grayscale values of images. Some additional properties are also calculated and used that are based on regional characteristics of various objects in the image. The need to retrieve the additional regional properties arises due to the fact that the standard histograms are insensitive to small changes in images. Many images of different types can have similar histograms, because, histograms provide only a coarse characterization of an image. This is the main disadvantage of using histograms. This research is based on the concept of Histogram Refinement (Pass and Zabih, IEEE Workshop Appl Comput Vision ( 1996 ), 96–102). Distributing the grayscale image intensities by splitting the pixels using their intensity values into several classes just like the histogram refinement method can provide an estimate of the object characteristics present in an image. After the calculation of clusters using a color refinement method, the inherent features of each of the clusters is calculated based on the regional properties of the clusters. These additional region based features expound some structural information of the image. Finally, all of these features are used for image retrieval.

Image Indexing using Spatial Multi-Resolution Color Correlogram

Color correlograms are efficiently used for image indexing in content-based image retrieval. Color correlogram extracts not only the color distribution of pixels in images like color histogram, but also extracts the spatial information of pixels in the images. The characteristic of the color correlogram to take into account the spatial information as well as the distribution of color pixels greatly attracts the researcher for content based image retrieval. Even though, a single correlogram is not enough for efficient and robust image retrieval system. In this paper, we propose the use of color correlogram on multiresolution images. The multiresolution color correlogram gives much better retrieval efficiency, but with higher computations. The multiresolution images are generated using the median filters.

Gesture Recognition Based on Neural Networks for Dance Game Contents

The purpose of this study was to propose the method to recognize gestures based on neural networks and inertia sensor which recognizes the motions of the user using inertia sensor and lets the user enjoy the game by comparing the recognized gestures with the pre-defined gestures for the dance game contents.