Object Extraction Research Papers - Academia.edu (original) (raw)

Three dimensional object extraction and recognition (OER) from LIDAR data has been an area of major interest in photogrammetry for quite a long time. However, most of the existing methods for automatic object extraction and recognition from LIDAR data are just based on the range information and employ parametric methods and object’s vagueness behaviour is basically neglected. Thus, these methods do not take into account the extraction and recognition complexities and may fail to reach a satisfied reliability level in complex situations. In this paper a novel approach based on the following strategies is formulated and implemented: (a) for a more comprehensive definition of the objects, information fusion concept is utilized, i.e., object’s descriptive components such as 3D structural and textural (ST) information are automatically extracted from first/last rang and intensity information of LIDAR data and simultaneously fed into the evaluation process, (b) for a more realistic expres...

- by
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- Fuzzy Logic, Recognition, LiDAR, Region growing

Axial X-ray computed tomography CT scanning provides a convenient means of recording the three-dimensional form of soil structure. The technique has been used for nearly two decades, but initial development has concentrated on qualitative description of images. More recently, increasing effort has been put into quantifying the geometry and topology of macropores likely to contribute to preferential flow in soils.

- by alain pierret
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- Soil Science, Image Analysis, Mathematical Morphology, Biological Sciences

The key obstacle to communicating images over wireless sensor networks has been the lack of suitable processing architecture and communication strategies to deal with the large volume of data. High packet error rates and the need for retransmission make it inefficient in terms of energy and bandwidth. This paper presents novel architecture and protocol for energy efficient image processing and communication over wireless sensor networks. Practical results show the effectiveness of these approaches to make image communication over wireless sensor networks feasible, reliable and efficient.

This paper presents a review of the current liter- ature on rough set and near set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm op- timization, support vector machines and fuzzy sets are also presented.

— This project deals with the exposition of how robotics can be applied to various fields of agriculture. One of the most important occupations in a developing country like India is agriculture. It is very important to improve the efficiency and productivity of agriculture by replacing laborer with intelligent machines like robots using latest technologies. The project proposes a new strategy to replace humans in various agricultural operations like detection of presence of pests, spraying of pesticides, spraying of fertilizers, etc. there by providing safety to the farmers and precision agriculture. The developed system involves designing a prototype which uses simple cost effective equipment's like microprocessors, wireless camera, various motors and terminal equipment's which is an aid to the farmers in various crop field activities.

- by IJSRD - International Journal for Scientific Research and Development and +1
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- Electrical Engineering, Image Analysis, Robots, Object Extraction

Process patterns represent well-structured and successful recurring activities of Software Development Methodologies (SDMs). They are able to form a library of reusable building blocks that can be utilized in Situational Method Engineering (SME) for constructing a custom SDM or enhancing an existing one to fit specific project situation. Recently, some researchers have subjectively extracted process patterns from existing SDMs based on cumulative experience in various domains; however, how to objectively extract process patterns from SDMs by adopting a systematic procedure has remained as question. In this regard, this paper is concerned with a procedure aiming to take process patterns out of existing SDMs. An example illustrates applicability of the proposed procedure for extracting process patterns in a specific context.

- by Dr. Mahdi Fahmideh
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- Software Engineering, Pattern Recognition, Method Engineering, Pattern Mining

Nowadays, remotely sensed images are used for various purposes in different applications. One of them is the cadastral application using high resolution satellite imagery. In this context, a comparison of extraction results from these images and existing vector data is the most important issue. The goal here is to show the advantages and disadvantages of the QuickBird and IKONOS imagery for making cadastral maps. In this study, high resolution IKONOS and QuickBird images of rural and urban test areas in Zonguldak and Bartın have been chosen. Firstly, pan-sharpened IKONOS and QuickBird images have been produced by fusion of high resolution PAN and MS images using PCI Geomatica v9.1 software package. The parcel, building and road network objects from these datasets have been extracted automatically by initially dividing it into segments and then being classified by using the spectral, spatial and contextual information in eCognition v4.0.6 software package. On the other hand, these ob...

- by Merve Marangoz
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- Image Processing, Remote Sensing, Cadastral Systems, Remote sensing and GIS

The recording and 3D modeling of large Cultural Heritage sites is currently receiving much attention. This has two main reasons. On the one hand UNESCO pays more attention to large cultural and natural sites and on the other hand we have a wide array of new technologies available which greatly support the efficient generation, administration and analysis of such models.

- by Armin Gruen
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- Cultural Heritage, Peru, New Technology, Data acquisition

We present a gait recognition system using infra-red (IR) images. Since an IR camera is not affected by the intensity of illumination, it is able to provide constant recognition performance regardless of the amount of illumination. Model-based object tracking algorithms enable robust tracking with partial occlusions or dynamic illumination. However, this algorithm often fails in tracking objects if strong edge exists near the object. Replacement of the input image by an IR image guarantees robust object region extraction because background edges do not affect the IR image. In conclusion, the proposed gait recognition algorithm improves accuracy in object extraction by using IR images and the improvements finally increase the recognition rate of gaits.

- by DaeHee Kim
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- Object Tracking, Active Shape Model, Object Extraction, Gait Recognition

A simple but very efficient technique of 3D object reconstruction and recognition is presented here. An optical and computer vision based measurement system is used to acquire the object's data. The recognition algorithm is a distance calculator. It is used to compute the proximity of the object's shape under test and a reference object's geometry shape. Here, as an application example, we presents a human face recognition case. The effectiveness of the algorithm is demonstrated here in two tests in which the same person is compared with himself within different facial expressions and with others persons with different faces expressions too. The results are presented in a comparative chart.

- by Jorge Barreto
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- Computer Vision, Face Recognition, Facial expression, Object Extraction

Declaration I certify that this thesis, which I now submit for examination for the award of PhD degree, is entirely my own work and has not been taken from the work of others, save and to the extent that, such work has been cited and acknowledged within the text of my work. This thesis was prepared according to the regulations for postgraduate study by research of the Dublin Institute of Technology and has not been submitted in whole or in part for another award in any Institute. The work reported on in this thesis conforms to the principles and requirements of the Institute's guidelines for ethics in research. The Institute has permission to keep, lend or copy this thesis in whole or in part, on condition that any such use of the material of the thesis be duly acknowledged.

In this paper we report on the results of two projects conducted recently for the AMilGeo (Amt für Militärisches Geowesen) of the German Federal Armed Forces. The aim of the first project was to establish an operational workflow to update existing VMap Level 1 data using commercially available satellite imagery. The complete workflow, primarily based on Intergraph software, was tested successfully with several examples and integrated into the existing VMap1 production environment. Additional software was developed for the feature linking of the "old" VMap1 data and the "new" topographic features extracted from the satellite images. The second project was a study focused on the generation and update of VMap Level 2 data using both satellite and airborne imagery. It was investigated in detail, which VMap2 features can be extracted from different satellite and airborne imagery by a human operator.

- by Wolfgang Reinhardt and +1
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- Feature Extraction, Satellite Imagery, Object Extraction, Level

This study examined the postulate that training production of syntactically complex sentences results in generalization to less complex sentences that have processes in common with treated structures. Three agrammatic aphasic patients were trained to produce wh-movement structures, object clefts and0or object extracted who-questions, while generalization between these structures was tested. One NP-movement structure, passive sentences, also was tested for control purposes. Wh-movement occurs from the direct object position to specifier position in the complementizer phrase [SPEC, CP] for both wh-movement structures. In who-questions movement occurs in the matrix sentence, whereas, in object clefts movement occurs within an embedded relative clause, rendering them the most complex. Results showed robust generalization effects from object clefts to matrix who-question for 1 participant (D.L.); however, no generalization was noted from who-questions to object clefts for another (F.P.), and 1 participant (C.H.) showed acquisition of who-questions, but not object clefts, during the baseline condition without direct treatment. As expected, none of the participants showed improved production of passives. These findings supported those derived from our previous studies, indicating that generalization is enhanced not only when target structures are related along dimensions articulated by linguistic theory, but also when the direction of treatment is from more to less complex structures. The present findings also support proposals that projections of higher levels in the syntactic treatment are dependent on successful projection of lower levels. For our participants, training movement within CP in a lower (embedded) clause resulted in their ability to project to CP at higher levels. (JINS, 1998, 4, 661-674.)

- by Kirrie Ballard
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- Treatment Outcome, The, Aged, Object Extraction

This work presents a method for liver isolation in magnetic resonance imaging (MRI) abdomen images. It is based on a priori statistical information about the shape of the liver obtained from a training set using the segmentation approach. Morphological watershed algorithm is used as a key technique as it is a simple and intuitive method, producing a complete division of the image in separated regions even if the contrast is poor, and it is fast, with possibility for parallel implementation. To overcome the over-segmentation problem of the watershed process, image preprocessing and postprocessing are applied. Morphological smoothing, Gaussian smoothing, intensity thresholding, gradient computation and gradient thresholding are proposed for preprocessing with morphological and graph based region adjacent list constructed for region merging. A new integrated region similarity function is also defined for region merging control. The proposed method produces good isolation of liver in axial MRI images of the abdomen, as is shown in this paper.

- by Rajasvaran Logeswaran and +1
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- Information Systems, Algorithms, Magnetic Resonance Imaging, Liver

Nowadays three dimension (3D) architectural visualisation has become a powerful tool in the
conceptualisation, design and presentation of architectural products in the construction industry, providing
realistic interaction and walkthrough on engineering products. Traditional ways of implementing 3D
models involves the use of specialised 3D authoring tools along with skilled 3D designers with blueprints of
the model and this is a slow and laborious process. The aim of this paper is to automate this process by
simply analyzing the blueprint document and generating the 3D scene automatically. For this purpose we
have devised a 3-Phase recognition approach to pseudo 3D building generation from 2D floor plan and
developed a software accordingly.
Our 3-phased 3D building system has been implemented using C, C++ and OpenCV library [24] for the
Image Processing module; The Save Module generated an XML file for storing the processed floor plan
objects attributes; while the Irrlitch [14] game engine was used to implement the Interactive 3D module.
Though still at its infancy, our proposed system gave commendable results. We tested our system on 6 floor
plans with complexities ranging from low to high and the results seems to be very promising with an
average processing time of around 3s and a 3D generation in 4s. In addition the system provides an
interactive walk-though and allows users to modify components.

This paper presents a review of the current literature on rough set and near set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

- by aboul ella hassanien
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- Engineering, Algorithms, Artificial Intelligence, Image Processing

Steganography is an emerging area which is used for secured data transmission over any public media.Steganography is a process that involves hiding a message in an appropriate carrier like image or audio. It is of Greek origin and means "covered or hidden writing". The carrier can be sent to a receiver without any one except the authenticated receiver knowing the existence of this information. In this paper, a specific image based steganography technique for communicating information more securely between two locations is proposed. The author incorporated the idea of secret key and password security features for authentication at both ends in order to achieve high level of security. As a further improvement of security level, the information has been permuted, encoded and then finally embedded on an image to form the stego image. In addition segmented objects extraction and reassembly of the stego image through normalized cut method has been carried out at the sender side and receiver side respectively in order to prevent distortion of the Stego image during transmission.

- by Mamta Juneja
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- Information Security, Object Extraction, Normalized Cut, Data Transmission

Sample-based music composition often involves the task of manually searching appropriate samples from existing audio. Audio mosaicing can be regarded as a way to automatize this process by specifying the desired audio attributes, so that sound snippets that match these attributes are concatenated in a synthesis engine. These attributes are typically derived from a target audio sequence, which might limit the musical control of the user.

- by Janer So Non
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- Object Classification, Object Extraction, Automatic Classification, Proof of Concept

In this paper, we propose a hierarchical state-based model for representing an echocardiogram video. It captures the semantics of video segments from dynamic characteristics of objects present in each segment. Our objective is to provide an effective method for segmenting an echo video into view, state, and substate levels. This is motivated by the need for building efficient indexing tools to support better content management. The modeling is done using four different views, namely, short axis, long axis, apical four chamber, and apical two chamber. For view classification, an artificial neural network is trained with the histogram of a region of interest of each video frame. Object states are detected with the help of synthetic M-mode images. In contrast to traditional single M-mode, we present a novel approach named sweep M-mode for state detection. We also introduce radial M-mode for substate identification from color flow Doppler 2-D imaging. The video model described here represents the semantics of video segments using first-order predicates. Suitable operators have been defined for querying the segments. We have carried out experiments on 20 echo videos and compared the results with manual annotation done by two experts. View classification accuracy is 97.19%. Misclassification error of the state detection stage is less than 13%, which is within acceptable range since only frames at the state boundaries are found to be misclassified.

- by aditi roy
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- Engineering, Algorithms, Artificial Intelligence, Content Management

This paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integrates the detection of foreground pixels with the conversion into labelled objects using a connected component labelling algorithm. The background models are based on 24-bit RGB values and 8-bit greyscale intensity values. A multimodal background differencing algorithm is presented, using a single FPGA chip and four blocks of RAM. The realtime connected component labelling algorithm, also designed for FPGA implementation, has efficiently been integrated with the pixel level background subtraction to extract pixels of a moving object as a single blob. The connected component algorithm, run-length encodes the binary image output of the background subtraction, and performs connected component analysis on this representation. The run-length encoding, together with other parts of the algorithm, is performed in parallel; sequential operations are minimized as the number of run-lengths are typically less than the number of pixels.

- by Patrick Dickinson and +1
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- Cognitive Science, Image Recognition (Computer Vision), Image segmentation, FPGA implementation

- by Abdi Jemal
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- Image Processing, Remote Sensing, Cadastre, Object Extraction

Forest fire is an important issue that damages thousands of hectares of forest and all creatures inside it. In this study we have developed an early fire detection system running on low-cost, lightweight Raspberry Pi module integrated with an infrared camera. The infrared camera acquires the live video of the fields, then if a fire occurs, our software detects the fire by taking both motion and color characteristics of a flames into account. In our study, there exist two phases: first, motion detection phase is applied to the view, if a motion is detected in the view, then, color detection phase is applied to determine the flames. Obtained results indicates that our fire detection system can detect a forest fire clearly and smoothly without requiring any external equipment and manual intervention

- by Emrullah YILDIRIM
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- Computer Vision, Fire Detecting System, Object Extraction, Image Proccessing

The analysis of remotely sensed data for object extraction is a key step in an increasing number of GIS (Geographic Information Science) applications, in particular for mapping, updating and change detection purposes. The main goal of this paper is to present an automatic method for detecting changes in a 2D building database, starting from recent satellite images. The workflow of our method is divided into two steps. 3D primitives, extracted from multiple images or from a correlation Digital Surface Model (DSM), are firstly collected for each building and matched with primitives derived from the existing database in order to achieve a final decision about acceptance or rejection. A specific algorithm, based on the DSM and a computed Digital Terrain Model (DTM), is subsequently used to extract new buildings. The method is here introduced and tested in two test areas, very different regarding the land use and topography. The outcomes of the method are assessed and show the good performance of our system, especially in terms of completeness, robustness and transferability.

- by Luigi Fregonese
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- Image Processing, Photogrammetry, LiDAR, Aerial Photogrammetry

In literature, several methods are available to combine both low spatial multispectral and low spectral panchromatic resolution images to obtain a high resolution multispectral image. One of the most common problems encountered in these methods is spectral distortions introduced during the merging process. At the same time, the spectral quality of the image is the most important factor affecting the accuracy of the results in many applications such as object recognition, object extraction, image analysis. In this study, the most commonly used methods including GIHS, GIHSF, PCA and Wavelet are analyzed using image quality metrics such as SSIM, ERGAS and SAM. At the same time, Wavelet is the best method for obtaining the fused image having the least spectral distortions according to obtained results. At the same time, image quality of GIHS, GIHSF and PCA methods are close to each other, but spatial qualities of the fused image using the wavelet method are less than others.

- by Altan Mesut
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- Geophysics, Algorithms, Image Processing, Remote Sensing

In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno). q

- by Roberto Tagliaferri and +2
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- Data Mining, Virtual Observatory, Soft Computing, Neural Networks

In three eye-movement monitoring experiments, participants' working memory capacity was assessed and they read sentences containing subject-extracted and object-extracted relative clauses. In Experiment 1, sentences lacked helpful semantic cues, object-relatives were harder to process than subject relatives, and working memory capacity did not moderate syntactic complexity eVects. In Experiments 2 and 3, categorical distinctions between critical nouns provided helpful semantic cues to syntactic structure and interpretation. Experiments 2 and 3 showed that helpful semantic cues reduced or eliminated syntactic complexity eVects, and that this reduction was not produced by lexical properties of spe-ciWc verbs. Further, in Experiment 2 working memory capacity moderated the interaction of syntactic complexity and semantic cues. 

- by Rihana Mason
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- Psychology, Cognitive Science, Semantics, Cognition

Light Detection and Ranging (LIDAR) systems have become a standard data collection technology for capturing object surface information and 3D modeling of urban areas. Although, LIDAR systems provide detailed valuable geometric information, they still require extensive interpretation of their data for object extraction and recognition to make it practical for mapping purposes. A fundamental step in the transformation of the LIDAR data into objects is the segmentation of LIDAR data through a clustering process. Nevertheless, due to scene complexity and the variety of objects in urban areas, e.g. buildings, roads, and trees, clustering using only one single cue will not reach meaningful results. The multi dimensionality nature of LIDAR data, e.g. laser range and intensity information in both first and last echo, allow the use of more information in the data clustering process and ultimately into the reconstruction scheme. Multi dimensionality nature of LIDAR data with a dense sampling interval in urban applications, provide a huge amount of valuable information. However, this amount of information produces a lot of problems for traditional clustering techniques. This paper describes the potential of an artificial swarm bee colony optimization algorithm to find global solutions to the clustering problem of multi dimensional LIDAR data in urban areas. The artificial bee colony algorithm performs neighborhood search combined with random search in a way that is reminiscent of the food foraging behavior of swarms of honey bees. Hence, by integrating the simplicity of the k-means algorithm with the capability of the artificial bee colony algorithm, a robust and efficient clustering method for object extraction from LIDAR data is presented in this paper. This algorithm successfully applied to different LIDAR data sets in different urban areas with different size and complexities.

- by Sara Saeedi
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- Honey bee, Data Collection, Data Clustering, Object Extraction

Several segmentation methods have been reported with their own pros and cons. Here we proposed a method for object extraction from T2 weighted (T2) brain magnetic resonance (MR) images. The proposed method is purely based on histogram processing for gradient calculation. The proposed method utilizes the histogram filtering technique as a pre-processing. The primary brain areas; gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are extracted out efficiently from 2D and 3D images. The method has been successfully implemented on human brain MR images obtained in clinical environment.

- by Muhammad Attique
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- Cognitive Science, Computer Vision, Biomedical Engineering, Image Processing

Abstract—Visual attention detection is an important technique in many computer vision applications. In this paper, we propose an algorithm to extract a salient object from an image using bottom-up and top-down computations. In bottomup computation, segment-based color contrast and attention values are employed to compose a bottom-up saliency map. In top-down computation, in-focus areas of the image are extracted to derive attention values using wavelet transforms for constructing a segment-based top-down saliency map. Attention values from both maps are combined by linear combination. The foreground/background-based salient object extraction is applied to form an output object. Experiments on 1,200 color images show that the proposed algorithm yields high level of satisfaction. I.

- by Wittawin Susutti
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- Computer Science, Computer Vision, Visual attention, Image segmentation
- by Pilar Merchán
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- Signal Processing, Pattern Recognition, Image Analysis, Localization

In this chapter, we explore shape representation, registration and modeling through implicit functions. To this end, we propose novel techniques to global and local registration of shapes through the alignment of the corresponding distance transforms, that consists of defining objective functions that minimize metrics between the implicit representations of shapes. Registration methods in the space of implicit functions like the Sum of Squares Differences (SSD) that can account for primitive transformations (similarity) and more advanced methods like mutual information that are able to handle more generic parametric transformations are considered. Toward addressing local correspondences we also propose an objective function on the space of implicit representations where the displacement field is represented with a free form deformation that can guarantee one-to-one mapping. In order to address outliers as well as introduce confidence in the registration process, we extend our registration paradigm to estimate uncertainties through the formulation of local registration as a statistical inference problem in the space of implicit functions. Validation of the method through various applications is proposed: (i) parametric shape modeling and segmentation through active shapes for medical image analysis, (ii) variable bandwidth non-parametric shape modeling for recognition and (iii) object extraction through a level set method. Promising results demonstrate the potentials of implicit shape representations.

Video surveillance always had a negative connotation, among others because of the loss of privacy and because it may not automatically increase public safety. If it was able to detect atypical (i.e. dangerous) situations in real time, autonomously and anonymously, this could change. A prerequisite for this is an automatic detection of possibly dangerous situations from video data. From the derived trajectories we then want to determine dangerous situations by detecting atypical trajectories. However, it is better to develop such a system without people being threatened or even harmed, plus with having them know that there is such a tracking system installed. In the artistic project leave a trace the tracked people become actor and thus part of the installation. Visualization in real-time allows interaction by these actors, which in turn creates many atypical interaction situations on which we could develop our situation detection.

- by Daniel Casasanto
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- Psychology, Cognitive Science, Music, Perception

This paper presents a new stochastic marked point process for describing images in terms of a finite library of geometric objects. Image analysis based on conventional marked point processes has already produced convincing results but at the expense of easy parameter tuning, short computing time, and unspecific models. Our more general multi-marked point process has simpler parametric setting, yields notably shorter computing times and can be applied to a variety of applications. Both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model, and a Jump-Diffusion process is performed to search for the optimal object configuration. Experiments with remotely sensed images and natural textures show the proposed approach has good potential. We conclude with a discussion about the insertion of more complex object interactions in the model by studying the compromise between model complexity and efficiency.

- by Georgy Gimel'farb
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- Information Systems, Algorithms, Artificial Intelligence, Remote Sensing

Shannon's definition of entropy is critically examined and a new definition of classical entropy based on the exponential behavior of information-gain is proposed along with its justification. The concept is then extended to gray tone image for defining its global, local and conditional entropy. Based on these definitions four algorithms for object extraction are developed and implemented. One of these algorithms uses a Poisson distribution-based model of an ideal image. Finally, a concept of positional entropy giving an information regarding the location of an object in a scene is introduced.

- by Nikhil Pal
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- Information Theory, Pattern Recognition, Entropy, Object Extraction

This article shows first results of implementing a method for creating DEMs from high resolution satellite imagery based on dynamic programming. The herein described DTW algorithm maps epipolar stereo image pairs line by line on top of each other using a method similar to dynamic time warping which is a common approach in speech recognition. The DTW algorithm is described and applied to several test images. The resulting DEMs and pros and cons of this method are shown and discussed.

- by Uwe Stilla
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- Remote Sensing, Archives, Orthophoto & DEM Generation, Satellite Data
- by Yu-Yin Hsu
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- Object Extraction

Automatic interesting object extraction is widely used in many image applications. Among various extraction approaches, saliency-based ones usually have a better performance since they well accord with human visual perception. However, nearly all existing saliency-based approaches suffer the integrity problem, namely, the extracted result is either a small part of the object (referred to as sketch-like) or a large region that contains some redundant part of the background (referred to as envelope-like). In this paper, we propose a novel object extraction approach by integrating two kinds of "complementary" saliency maps (i.e., sketch-like and envelope-like maps). In our approach, the extraction process is decomposed into two sub-processes, one used to extract a high-precision result based on the sketch-like map, and the other used to extract a high-recall result based on the envelope-like map. Then a classification step is used to extract an exact object based on the two results. By transferring the complex extraction task to an easier classification problem, our approach can effectively break down the integrity problem. Experimental results show that the proposed approach outperforms six state-ofart saliency-based methods remarkably in automatic object extraction, and is even comparable to some interactive approaches.

- by Farhad Samadzadegan and +1
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- Honey bee, Data Collection, Data Clustering, Object Extraction

This paper reports the results of five experiments designed to investigate the effects of referential processing on sentence complexity. Gibson (Cognition, 68 (1998) 1) suggested that sentence complexity is related to the locality of integrations between dependent syntactic heads, and that an appropriate measure of locality is the number of new discourse referents intervening between the endpoints of those integrations. The experiments in this paper test, modify and extend claims. Each experiment manipulated noun phrases (NPs) in the subject positions of objectextracted relative clauses in order to determine how different types of NPs affected sentence complexity. Experiments 1, 2 and 3 used questionnaires to gauge sentence complexity, whereas Experiments 4 and 5 used self-paced reading. The results from Experiments 1, 2, 4 and 5 suggest that the complexity of the experimental items was more closely related to the Givenness status of the embedded subject in the Givenness Hierarchy than to whether the embedded subject was old or new to the discourse. Experiment 3 compared materials in which a quantifier was rotated through subject positions of a nested relative clause structure. The results of this experiment support a discourseprocessing-based distance metric for computing locality and provide evidence against a pure similarity-based account of structural complexity such as proposed by Bever (Bever, T. G. (1970). The cognitive basis of linguistic structures. In J. R. Hayes (Ed.), Cognition and the development of language (pp. 279-362). New York: Wiley). q a review of relevant literature). For example, the sentences in (1) are increasingly complex:

- by Edward Gibson
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- Cognition, Speech perception, Experience Design, Linguistics

There are many image fusion methods that can be used to produce high-resolution mutlispectral images from a high-resolution panchromatic image and low-resolution mutlispectral images. Starting from the physical principle of image formation, this paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods. Using the GIF method, it is shown that the pixel values of the high-resolution mutlispectral images are determined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of the high-resolution panchromatic image at the low-resolution level. Many of the existing image fusion methods, including, but not limited to, intensity-hue-saturation, Brovey transform, principal component analysis, high-pass filtering, high-pass modulation, the à trous algorithm-based wavelet transform, and multiresolution analysis-based intensity modulation (MRAIM), are evaluated and found to be particular cases of the GIF method. The performance of each image fusion method is theoretically analyzed based on how the corresponding low-resolution panchromatic image is computed and how the modulation coefficients are set. An experiment based on IKONOS images shows that there is consistency between the theoretical analysis and the experimental results and that the MRAIM method synthesizes the images closest to those the corresponding multisensors would observe at the high-resolution level.

- by Mufit Cetin
- •
- Geophysics, Algorithms, Image Processing, Remote Sensing

Image segmentation and object extraction plays an important role in image analysis and computer vision. In this paper, we propose a novel technique for color image segmentation called 'adaptive neuro-fuzzy color image seg- mentation (ANFCIS)'.The proposed system consists of multilayer perceptron (MLP)-like network which performs color image segmentation using multilevel thresholding. Threshold values for detecting clusters and their labels are

- by Ganeshchandra Shinde
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- Computer Vision, A Priori Knowledge, Image Analysis, Neural Network
- by Koustubh Sinkar
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- Image Processing, OpenCV, High Resolution Images, Object Extraction

An efficient hierarchical visual representation is proposed for progressive content-based image indexing and retrieval. More specifically, the image content is decomposed at different description levels, each of which represents the image at a different content scale. This is implemented by partitioning the image into objects structured in a hierarchical way. The lowest description level is obtained by applying a color segmentation algorithm to the image. On the other hand, the highest level is achieved by a semantic segmentation implemented in a semi-automatic framework. The spatial relationship of the object extracted, at a given description level, is described by a graph structure scheme. In particular, for each object (segment) a graph node is constructed and includes several features of that object. The graph links indicate the spatial relationship of the object with its neighbors. Following the hierarchical object decomposition, a tree-structure is created, providing efficient representation of the visual content.

- by Stefanos Kollias
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- Computer Science, Information Retrieval, Data Mining, Image segmentation

- by Muhammad Dawood
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- Image Processing, Pattern Recognition, Recognition, Construction Industry

Using Individual Human Knowledge Model (IHK model), which is designed focusing on the difference of each human's knowledge, we analyzed the results and design process of an application of Method of Object Extraction for Architecture (OEA method) to real house building project. OEA method is a new method aiming to realize an architecture free from the existing

- by Yukari Nagai
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- Design process, Object Extraction