Object Extraction System Research Articles

A Comparative Study on the Development of Binary Object Extraction System using Different Self Organizing Neural Network

Accurately Extraction of a binary object from a noisy perspective has been a daunting task in the field of pattern recognition. Several techniques have been tried to optimally solve the problem of denoising of object over the decades. In this paper, different binary object extraction methods are reviewed which are basically guided by different SelfOrganizing Neural Networks (SONN) architectures as BiDirectional Self Organizing Neural Network (BDSONN), multi-Layer Self Organizing neural Network (MLSONN) and quantum version of MLSONN (QMLSONN). The result shows that QMLSONN outperforms over other network architectures in terms of time and also it restores shape of the object with great accuracy.

Unsupervised Object Extraction by Contour Delineation and Texture Discrimination Based on Oriented Edge Features

This paper presents an unsupervised object extraction system that extracts a single object from natural scenes without relying on color information. The contour information and texture information are analyzed through separate oriented-edge-based processing channels and then combined to complement each other. Contour candidates are extracted from multiresolution edge maps, whereas the local texture information is compactly represented by an oriented-edge-based feature vector and then analyzed by K-means clustering. The object region is determined by merging the results of two separate analysis channels based on the simple assumption that the object is located centrally in the scene. As a result, the object region has been successfully extracted from the scene with a well-defined single boundary line. Both subjective and objective evaluations were carried out and it is shown that the proposed algorithm handles the challenges of complex background well, using only gray-scale images.

An Automated Video Object Extraction System Based on Spatiotemporal Independent Component Analysis and Multiscale Segmentation

Video content analysis is essential for efficient and intelligent utilizations of vast multimedia databases over the Internet. In video sequences, object-based extraction techniques are important for content-based video processing in many applications. In this paper, a novel technique is developed to extract objects from video sequences based on spatiotemporal independent component analysis (stICA) and multiscale analysis. The stICA is used to extract the preliminary source images containing moving objects in video sequences. The source image data obtained after stICA analysis are further processed using wavelet-based multiscale image segmentation and region detection techniques to improve the accuracy of the extracted object. An automated video object extraction system is developed based on these new techniques. Preliminary results demonstrate great potential for the new stICA and multiscale-segmentation-based object extraction system in content-based video processing applications.

A methodical approach to extracting interesting objects from dynamic web pages

This paper presents a fully automated object extraction system for web documents. Our methodology consists of a layered framework and a set of algorithms. A distinct feature of our approach is the full automation of both the extraction of data object regions from dynamic web pages and the identification of the correct object-boundary separators. We implemented the methodology in the XWRAPElite object extraction system and evaluated the system using more than 3200 pages over 75 diverse websites. Our experiments show three important and interesting results: First, our algorithms for identifying the minimal object-rich subtree achieves a 96% success rate over all the web pages we have tested. Second, our algorithms for discovering and extracting object separator tags reach the success rate of 95%. Most significantly, the overall system achieves a precision between 96% and 100% (it returns only correct objects) and excellent recall (between 95% and 96%, with very few significant objects left out). The minimal subtree extraction algorithms and the object-boundary identification algorithms are fast, about 87 milliseconds per page with an average page size of 30KB.

Accessing Video Contents through Key Objects over IP

In order to support content-based video database access over the Internet Protocol (IP), achieving the following objectives are important: (i) video query by a representative object (key object) or some statistical characterization of the target contents, (ii) bandwidth-efficient browsing over IP, and (iii) scalable and user-centric video transmission over a heterogeneous and variable-bandwidth network. We present a video object extraction and scalable coding system designed to meet the above objectives. In our system, key objects of meaning to video database users are generated via a human-computer-interaction procedure, and are tracked across frames. Given a key object, an algorithm classifies a subset of its VOPs as key VOPs. This subset forms the basis of a highly bandwidth-efficient base layer for supporting activities such as browsing and refining queries. Over the base layer, a number of enhancement layers can be defined to progressively increase the spatial and temporal resolutions of retrieved video. It is expected that heterogeneous users can subscribe to different numbers of the enhancement layers according to their own conditions, such as access authorization, available connection bandwidth, and quality preference.

Extracting semantic video objects

We present an accurate and user-interactive semantic video object (SVO) extraction system. Although we also obtain an SVO with an accurate boundary by integrating temporal and spatial information, our way is quite different from others' work. Instead of fusing spatial and temporal segmentations on the first or all the frames of a video sequence, our system adaptively performs spatial and temporal segmentation and fusion when necessary. To achieve this, our system detects the variations between successive frames. We only need to fuse the spatial and temporal segmentation when a large variation occurs. Otherwise, the system tracks the previous SVO's boundary. We find this simple method efficient in both speed and accuracy. Since the temporal segmentation, spatial segmentation, spatio-temporal fusion, and boundary tracking all employ simple algorithms, our system has a low computational complexity.

Semiautomatic segmentation and tracking of semantic video objects

This paper introduces a novel semantic video object extraction system using mathematical morphology and a perspective motion model. Inspired by the results from the study of the human visual system, we intend to solve the semantic video object extraction problem in two separate steps: supervised I-frame segmentation, and unsupervised P-frame tracking. First, the precise semantic video object boundary can be found using a combination of human assistance and a morphological segmentation tool. Second, the semantic video objects in the remaining frames are obtained using global perspective motion estimation and compensation of the previous semantic video object plus boundary refinement as used for I frames.

Levels of knowledge for object extraction

A paradigm for image interpretation and object extraction that deals explicitly with the levels of abstraction of such processes is described. The central new feature of this paradigm is two intermediate-level processes that deal with construction of symbolic image tokens and with symbolic descriptions of quantities. These intermediate-level processes have access to digital data but produce symbolic, abstract tokens and descriptions. The object extraction system is controlled by a higher-level symbolic process that uses knowledge-based reasoning.