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To improve remote sensing image classification precision, we propose a novel method which is based on super pixel and adaptive weighted K-Means. First, super pixel segmentation algorithm is used to divide input images into irregular blocks which remain their semantic information and boundaries. And then, SIFT, GIST, Census, Gabor, and Color histogram, and many other types of features are extracted...
Image segmentation is an image processing technology, which decomposes the image into several regions with their own characteristic for the sake of extracting useful target, and it is a key step from the image processing to image analysis. This paper mainly implements a system about applying ant colony algorithm to image segmentation, which is based on the aspects of the discreteness of digital image...
In this paper, a novel surveillance video summaryzation approach is proposed to detect the objects and targets which appear less frequently. This approach integrates clustering and background subtraction. The clustering method adopts a modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to summarize the surveillance video with HSV color feature. Then the background...
Traversable region detection is important for autonomous visual navigation of mobile robots. Only short range traversable regions can be detected using traditional methods based on stereo vision because of the limited image resolution and baseline of stereo vision. In this paper, we propose a novel method to detect long range traversable regions without using any supervised or self-supervised learning...
Image segmentation is an important research topic in the field of computer vision. Spectral Clustering (SC) algorithm is one of the most popular used clustering methods for image segmentation. However, the cluster number must be estimated by expertise users to be determined. This limits its application in image segmentation. In this paper, we proposed an image segmentation method based on saliency...
Video Shot Detection is used to determine the scene changes in the video. Video Summarization provides most informative frames to generate the abstract of the input video. In this paper, we have integrated both the concepts for the better outcome of the results. The Color Layout Descriptor and Expected-Maximization are used for the Video Shot Detection and Video summarization respectively. The output...
There are a large number of colors to represent images (e.g. 256256256 = 16,777,216 colors in an RGB color space) on computers. Since there are too many colors to handle, a large number of colors are reduced by quantization in the image processing in general. When we perform a uniform color quantization, we often get colors which do not fit the real world. Therefore, typical colors should be learned...
Automatic text detection and extraction systems for natural scene images and videos have gained wide attention due to its immense applications in various fields of information retrieval. Many algorithms have been proposed in literature which addresses the problem of text detection. The color uniformity of text characters is one of the strong features which is used in color based text localisation...
The unstructured road detection plays a key role in an autonomous vehicle navigation system. However, the unstructured road images often contain shadows and are easily affected by ambient light, resulting to an inaccuracy with road detection. A robust road detection technique is required. In this paper, we adopted an improved fuzzy c-means(FCM) clustering algorithm to address these issues. The new...
Clustering is a hotspot issues in the field of data mining. There is abundant digital image information in the image acquisition equipment, the image database or the Internet. Facing the large scale image information with rich semantics, it is difficult to obtain accurate information as soon as possible. Therefore, it is essential for us to study efficient image clustering algorithms, in which how...
This paper proposes an approach which combines the Decision Theoretic Rough Set model (DTRS) and Fuzzy C-Means(FCM) algorithm to perform color image segmentation. The FCM algorithm has the limitation that it requires the initialization of cluster centroids and the number of clusters. In this paper, the DTRS model is applied to color image segmentation for the purpose of clustering validity analysis...
Foreground segmentation has been widely used in many computer vision applications, and background substraction(BS) is one of most widely used routines. In order to achieve satisfactory completeness and robustness of foreground, in this paper, we propose a method which uses motion-based contours to enhance the existing kinds of BS algorithms in the area which is low contrast. First we obtain key regions...
In this paper we present a variant of Hough voting for detecting and parsing image objects into their constituent symmetrical parts. The parsing algorithm, given an input image, first uses the Hough voting scheme to detect the salient symmetrical parts by an integrating of color segmentation, depth coherence, and motion grouping. The output of the parsing algorithm is an object graph which is further...
Search and retrieval of images based on content has attracted considerable attention in recent years from the research community. Classification and Clustering algorithm are used to improve the result of Content based Image retrieval. This paper relies on a combination of color and edge features of image for the accurate retrieval of images. Color features are extracted by RGB color histogram and...
The objective of this paper is twofold. First, we introduce an effective region-based solution for saliency detection. Then, we apply the achieved saliency map to better encode the image features for solving object recognition task. To find the perceptually and semantically meaningful salient regions, we extract superpixels based on an adaptive mean shift algorithm as the basic elements for saliency...
Recent advances in technology have made tremendous amount of multimedia information available to the general population. To access the needed information in this scenario there is a need for automatic tools to filter and present information summary. Summarization techniques will give a choice to users to browse and select the multimedia documents of their choice for complete viewing later. In this...
Presented is a coupled L∞ filter capable of simultaneously estimating model parameters for photo-geometric registration between two (or more) images. We also introduce a colour code based clustering technique to group features for fast and reliable matching. This technique is based on the CIE colour model and is invariant to illumination changes. An increase in speed of almost 2 times is observed...
Terrestrial laser scanning (TLS, also called ground based Light Detection and Ranging, LIDAR) is an effective data acquisition method capable of high precision, detailed 3D models for surveying natural environments. However, despite the high density, and quality, of the data itself, the data acquired contains no direct intelligence necessary for further modeling and analysis - merely the 3D geometry...
The objective of this paper is to develop a real-time unsupervised learning method to detect multi-objects. Variance and gradient variance, as main texture feature, are compressed based on PCA (Principal Component Analysis) to get initial classifications of clusters via K-means algorithm in the image frame. The cluster-kernel of each class, the nucleus as we defined, is figured out through shifting...
This paper represents unsupervised method of color based segmentation using clustering to classify vegetated and urban area in Satellite images. Now a day due to the progresses in spatial resolution of satellite imagery, the methods of segment-based image analysis for generating and updating geographical information are becoming more important. In this work, one method proposed a segmentation of various...
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