Approach For Object Extraction Research Articles

Object extraction from image with big size based on bilateral grid

Currently image with big size segmentation suffers from low efficiency and inaccurate results, in this paper a novel object extraction approach from image with big size based on the application of a bilateral grid is proposed. Firstly, the bilateral grid is constructed according to the spatial ratio and color sampling ratios, which are determined by both image size and color range. Then, the big image data is splatted into the bilateral grid, the sampled data among the grid vertices are assigned by the nearest vertex data, and all grid vertices are assigned the object label via random forest classification. Finally, the segmentation is reconstructed from the grid data by interpolation. Experiment results show that the proposed algorithm could effectively improve the big image segmentation efficiency, and achieve the better segmentation results than state-of-the-art methods.

Moving objects extraction for building environmental maps describing human walking activity using a 3D LiDAR

ABSTRACT This study proposes an indoor movable static object extraction approach for building environmental maps that describe indoor human activities. In particular, it is essential to distinguishing people from non-human movable objects. To address this issue, we classify static objects into three categories based on their features. Methods for extracting movable static objects using each feature are presented. We conducted experiments and confirmed that the movable static objects were successfully detected and redefined as backgrounds, and only people were determined as dynamic objects.

An Extended Random Walker Approach for Object Extraction by Integrating VGI Data and VHR Image

Automatic extraction of objects in urban areas from very-high-resolution (VHR) images is of great significance to many applications. Existing approaches consider little information on spatial relationships, backgrounds, and prior knowledge of target objects, leading to that they did not perform well in object extraction. Now free and fast growing volunteered geographic information (VGI) can be accessed easily; thus, they can be used as prior information to improve the performance. This study develops an extended random walker (RW) approach to form a bottom-up and top-down mechanism for extracting target objects by combining VHR images and VGI data. Novel aspects of our approach include: 1) both the shape and spectral prior terms are incorporated into the extended RW algorithm; 2) an end-to-end framework is proposed to automatically select both foreground and background seeds with the assistance of VGI data; and 3) the shape prior of VGI data provides top-down information to select background and foreground seeds and help fuse bottom-up image information (i.e., foreground and background seeds and spatial relationships) to extract target objects. The extended RW approach was validated on building and lake datasets, and its performance is evaluated on both pixel and object levels. Quantitative comparisons with the original RW and random forest (RF) algorithm indicate that the proposed approach achieves significant better performance. Besides, it can successfully extract the partly occluded buildings.

Image Fusion Based on Wavelet Transforms

The image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than any of the input images. A new approach for object extraction from high-resolution images is presented in this report. In this paper, we have presented image fusion based on wavelet transform. Medical Image Fusion is a sort of data fusion and developed from that into a new data fusion technology. As an advanced method of image processing technology to integrate multi-source image data, image fusion is to integrate two or more images into a new fusion image. The aim of fusion is the combination of images to integrate the information of each individual technique and reduce the uncertainty of the image information. Computed tomography (CT), and positron emission tomography (PET) provide data conditioned by the different technical, anatomical and functional properties of the organ or tissue being studied, with values of sensitivity, specificity and diagnostic accuracy variations between them. Their fusion enables the unification of the various technique-dependent data, thus summing the diagnostic potential of each individual technique. Because of the image fusion technology which can effectively integrate the image information, the fusion images are more intelligible and readable and have more information than the images that are got through single channel, and this technology has been concentrated very much, and has had a great development. Truly the image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than any of the input images. A new approach for object extraction from high-resolution images is presented in this report. In this we present a new approach to better extract information from CT (Computed Tomography) /PET (positron emission tomography) which will be helpful to diagnose diseases. Since there are various methods and algorithms for fusion of these images and as there are special advantages for each algorithm along with ever growing need to use of this technique, as a result research about this field of study becomes more sophisticated. Many methods exist to perform image fusion. The very basic ones are the Probabilistic Approximation model. The wavelet approach gives better accuracy and increased information compared to the previous techniques.

GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images

This paper proposes an object-based classification scheme for handling remotely sensed images. The method combines the results of a supervised pixel-based classifier with spatial information extracted from image segmentation. First, pixel-wise classification is implemented by a fuzzy output SVM classifier using spectral and textural features of pixels. This classification results to a set of fuzzy membership maps. Operating on this transformed space, a Genetic Sequential Image Segmentation (GeneSIS) algorithm is next developed to partition the image into homogeneous regions. GeneSIS follows a sequential object extraction approach, whereby at each iteration a single object is extracted by invoking a GA-based object extraction algorithm. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between three fuzzy components: coverage, consistency and smoothness. The final classification map is obtained automatically via segmentation, since each segment is extracted with its own class label. The validity of the proposed method is shown on the land cover classification of three different remote sensing images, with varying number of spectral bands (multispectral/hyperspectral), different spatial resolutions and ground truth cover types. The accuracy results of our approach are favorably compared with the ones obtained by other segmentation-based classification techniques.

Automatic fuzzy object-based analysis of VHSR images for urban objects extraction

We present an automatic approach for object extraction from very high spatial resolution (VHSR) satellite images based on Object-Based Image Analysis (OBIA). The proposed solution requires no input data other than the studied image. Not input parameters are required. First, an automatic non-parametric cooperative segmentation technique is applied to create object primitives. A fuzzy rule base is developed based on the human knowledge used for image interpretation. The rules integrate spectral, textural, geometric and contextual object proprieties. The classes of interest are: tree, lawn, bare soil and water for natural classes; building, road, parking lot for man made classes. The fuzzy logic is integrated in our approach in order to manage the complexity of the studied subject, to reason with imprecise knowledge and to give information on the precision and certainty of the extracted objects. The proposed approach was applied to extracts of Ikonos images of Sherbrooke city (Canada). An overall total extraction accuracy of 80% was observed. The correctness rates obtained for building, road and parking lot classes are of 81%, 75% and 60%, respectively.

An Object Extraction Approach for Impervious Surface Classification with Very-High-Resolution Imagery

Detailed land cover maps provide important information for research and decision-making but are often expensive to develop and can become outdated quickly. Widespread availability of aerial photography provides increased accessibility of high-resolution imagery and the potential to produce high-accuracy land cover classifications. However, these classifications often require expert knowledge and are time consuming. Our goal was to develop an efficient, accurate technique for classifying impervious surface in urbanizing Wake County, North Carolina. Using an iterative training technique, we classified 111 nonmosaicked, very-high-resolution images using the Feature Analyst software developed by Visual Learning Systems. Feature Analyst provides object extraction classifications by analyzing spatial context in relation to spectral data to classify high-resolution imagery. Our image classification results were 95 percent accurate in impervious surface extraction, with an overall total accuracy of 92 percent. Using this method, users with relatively limited geographic information system (GIS) training and modest budgets can produce highly accurate object-extracted classifications of impervious and pervious surface that are easily manipulated in a GIS.