The return of object-based attention: Selection of multiple-region objects

2006 ◽  
Vol 68 (7) ◽  
pp. 1163-1175 ◽  
Author(s):  
Michi Matsukura ◽  
Shaun P. Vecera
Keyword(s):  
Object Based ◽  
Attention Selection ◽  
Selection Of

scholarly journals Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

2018 ◽  
Vol 10 (8) ◽  
pp. 1285 ◽  
Author(s):  
Reza Attarzadeh ◽  
Jalal Amini ◽  
Claudia Notarnicola ◽  
Felix Greifeneder
Keyword(s):  
Soil Moisture ◽  
Optical Data ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Practical Applications ◽  
Initial Stage ◽  
Object Based ◽  
Single Polarization ◽  
Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

TRIZ Application in Bionic Modeling for Lightweight Design of Machine Tool Column

2018 ◽  
Author(s):  
Shenggang Guo ◽  
Zhiling Yuan ◽  
Fenghe Wu ◽  
Yongxin Li ◽  
Shaoshuai Wang ◽  
...  
Keyword(s):  
Machine Tool ◽  
Lightweight Design ◽  
Biological Database ◽  
Biological Phenomenon ◽  
Bionic Design ◽  
Evaluation Theory ◽  
Column Structure ◽  
Object Based ◽  
Heavy Machine ◽  
Selection Of

The selection of biomimetic prototypes mostly depends on the subjective observation of a designer. This research uses TRIZ to explore some inferential steps in bionic design of the heavy machine tool column. Conflict resolution theory of TRIZ is applied to describe improved and deteriorated parameters and a contradiction matrix is used to obtain recommended inventive principles. A reference table of solutions corresponding to the biological phenomenon and TRIZ solutions is formed to expedite retrieving the biomimetic object. Based on the table, herbaceous hollow stem is selected to imitate column structure. Four kinds of plant are chosen from the biological database. To select the best from four candidates, a bionic ideality evaluation index is proposed based on similarity analysis and ideality evaluation theory in TRIZ. Thus, the bionic effect can be described and compared quantitatively. Bionic configuration is then evolved concerning manufacturing requirements. Size optimization of stiffener thicknesses is implemented finally, and satisfactory results of the lightweight effect is obtained.

Neural Mechanisms of Visual Attention: Object-Based Selection of a Region in Space

2000 ◽  
Vol 12 (supplement 2) ◽  
pp. 106-117 ◽  
Author(s):  
Catherine M. Arrington ◽  
Thomas H. Carr ◽  
Andrew R. Mayer ◽  
Stephen M. Rao
Keyword(s):  
Visual Attention ◽  
Spatial Attention ◽  
Brain Activity ◽  
Temporal Cortex ◽  
Brain Structures ◽  
Brain Regions ◽  
Brain Areas ◽  
Unbounded Region ◽  
Object Based ◽  
Selection Of

Objects play an important role in guiding spatial attention through a cluttered visual environment. We used event-related functional magnetic resonance imaging (ER-fMRI) to measure brain activity during cued discrimination tasks requiring subjects to orient attention either to a region bounded by an object (object-based spatial attention) or to an unbounded region of space (location-based spatial attention) in anticipation of an upcoming target. Comparison between the two tasks revealed greater activation when attention selected a region bounded by an object. This activation was strongly lateralized to the left hemisphere and formed a widely distributed network including (a) attentional structures in parietal and temporal cortex and thalamus, (b) ventral-stream object processing structures in occipital, inferior-temporal, and parahippocampal cortex, and (c) control structures in medial-and dorsolateral-prefrontal cortex. These results suggest that object-based spatial selection is achieved by imposing additional constraints over and above those processes already operating to achieve selection of an unbounded region. In addition, ER-fMRI methodology allowed a comparison of validly versus invalidly cued trials, thereby delineating brain structures involved in the reorientation of attention after its initial deployment proved incorrect. All areas of activation that differentiated between these two trial types resulted from greater activity during the invalid trials. This outcome suggests that all brain areas involved in attentional orienting and task performance in response to valid cues are also involved on invalid trials. During invalid trials, additional brain regions are recruited when a perceiver recovers from invalid cueing and reorients attention to a target appearing at an uncued location. Activated brain areas specific to attentional reorientation were strongly right-lateralized and included posterior temporal and inferior parietal regions previously implicated in visual attention processes, as well as prefrontal regions that likely subserve control processes, particularly related to inhibition of inappropriate responding.

scholarly journals Object-based Selection of Irrelevant Features Is Not Confined to the Attended Object

2011 ◽  
Vol 23 (9) ◽  
pp. 2231-2239 ◽  
Author(s):  
Carsten N. Boehler ◽  
Mircea A. Schoenfeld ◽  
Hans-Jochen Heinze ◽  
Jens-Max Hopf
Keyword(s):  
Visual Search ◽  
Visual Field ◽  
Target Object ◽  
Neural Processing ◽  
Irrelevant Feature ◽  
Distractor Object ◽  
Object Based ◽  
Irrelevant Color ◽  
Task Irrelevant ◽  
Selection Of

Attention to one feature of an object can bias the processing of unattended features of that object. Here we demonstrate with ERPs in visual search that this object-based bias for an irrelevant feature also appears in an unattended object when it shares that feature with the target object. Specifically, we show that the ERP response elicited by a distractor object in one visual field is modulated as a function of whether a task-irrelevant color of that distractor is also present in the target object that is presented in the opposite visual field. Importantly, we find this modulation to arise with a delay of approximately 80 msec relative to the N2pc—a component of the ERP response that reflects the focusing of attention onto the target. In a second experiment, we demonstrate that this modulation reflects enhanced neural processing in the unattended object. These observations together facilitate the surprising conclusion that the object-based selection of irrelevant features is spatially global even after attention has selected the target object.

scholarly journals Object-based selection in visual working memory

2021 ◽  
Author(s):  
Yin-ting Lin ◽  
Garry Kong ◽  
Daryl Fougnie
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Spatial Distance ◽  
Object Selection ◽  
Object Based ◽  
Memory Experiment ◽  
Object Feature ◽  
Feature Dimension ◽  
Selection Of ◽  
Attentional Mechanisms

AbstractAttentional mechanisms in perception can operate over locations, features, or objects. However, people direct attention not only towards information in the external world, but also to information maintained in working memory. To what extent do perception and memory draw on similar selection properties? Here we examined whether principles of object-based attention can also hold true in visual working memory. Experiment 1 examined whether object structure guides selection independently of spatial distance. In a memory updating task, participants encoded two rectangular bars with colored ends before updating two colors during maintenance. Memory updates were faster for two equidistant colors on the same object than on different objects. Experiment 2 examined whether selection of a single object feature spreads to other features within the same object. Participants memorized two sequentially presented Gabors, and a retro-cue indicated which object and feature dimension (color or orientation) would be most relevant to the memory test. We found stronger effects of object selection than feature selection: accuracy was higher for the uncued feature in the same object than the cued feature in the other object. Together these findings demonstrate effects of object-based attention on visual working memory, at least when object-based representations are encouraged, and suggest shared attentional mechanisms across perception and memory.

scholarly journals Kombinasi Indeks Citra untuk Analisis Lahan Terbangun dan Vegetasi Perkotaan

2018 ◽  
Vol 32 (1) ◽  
pp. 24 ◽  
Author(s):  
Iswari Nur Hidayati ◽  
R. Suharyadi ◽  
Projo Danoedoro
Keyword(s):  
Data Extraction ◽  
Urban Land ◽  
Index Formula ◽  
Vegetation Density ◽  
Threshold Values ◽  
Building Density ◽  
Object Based ◽  
Pca Method ◽  
Normalized Difference Index ◽  
Selection Of

Lahan terbangun di perkotaan dan area vegetasi menjadi hal yang sangat menarik untuk dikaji. Apalagi dinamika penggunaan lahan di perkotaan yang sangat cepat berubah. Berbagai metode dikembangkan untuk ekstraksi lahan terbangun di perkotaan, mulai dari klasifikasi multispektral, object based approach, hingga penelitian berbasis indeks. NDBI menjadi salah satu indeks pioner untuk ekstraksi lahan terbangun perkotaan dengan menggunakan saluran SWIR. Pengembangan indeks lahan terbangun ini masih perlu dikembangan untuk citra yang tidak mempunyai panjang gelombang SWIR. Tujuan penelitian ini adalah merumuskan kombinasi saluran terbaik dalam ekstraksi lahan terbangun dan area vegetasi serta menghitung kepadatan bangunan dan kerapatan vegetasi berbasis indeks. Penelitian ini menggunakan Citra Worldview-2 yang diperoleh dari Digital Globe Foundation untuk ekstraksi lahan terbangun dan kerapatan vegetasi. Normalized difference index digunakan sebagai formula dalam pembuatan indeks. Pemanfaatan semua saluran spektral dalam citra Worldview-2 digunakan untuk ekstraksi lahan terbangun dan kepadatan bangunan di perkotaan dengan PCA sebagai metode untuk penggabungan delapan saluran dalam Worldview-2. Saluran NIR 1 dan NIR 2 yang digabungkan dengan Saluran Merah menjadi pilihan untuk ekstraksi vegetasi. Proses trial dan error mewarnai pemilihan kombinasi saluran yang digunakan dan treshold yang digunakan untuk analisis biner dalam membedakan lahan terbangun dan non lahan terbangun serta area vegetasi dan area non vegetasi. Pemanfaatan unique identification (UID) digunakan untuk pembuatan grid berbasis raster dalam perhitungan kepadatan bangunan dan kerapatan vegetasi. Hasil penelitian menunjukkan bahwa indeks yang dibangun dengan PC2 dan NIR 1 serta PC2 dan NIR 2 mempunyai akurasi tinggi yaitu 94,43% untuk bangunan dan kombinasi indeks dari NIR1_Red mempunyai akurasi optimal yaitu 99,51% dan NIR2_Red mempunyai akurasi 92,87 untuk ekstraksi data vegetasi.  Urban phenomenon becomes a very interesting thing to be studied. The urban land use, land conversion, urban green space, are rapidly changing. Various methods were developed for urban built-up data extraction, such as multispectral classification, object-based approach, and index-based research. NDBI became one of pioneer indices for urban-built urban land extraction using SWIR band. The development of this built-up index is still required for images that do not have SWIR wavelengths. The study objectives were to select the best methods for built-up land and vegetation extraction and to calculate building density and index-based vegetation density. Worldview-2 image obtained from Digital Globe Foundation tested for built-up land data extracting and vegetation density analyzing. Normalized difference index formula is applied for combining and setting built-up land and vegetation indexes. Merger of Worldview-2 spectral imagery were using PCA method for extracting built-up land and calculating building density. Combining eight bands into eight new images that have different information from original images was done by PCA method.  NIR 1, NIR2, and Red bands are the perfect choice for vegetation extraction because near infrared characteristics have high reflections on vegetation. Selection of band combinations and selection of threshold values through trial and error processes to perceive the best index combinations and reasonable threshold values. Binary analysis is particularly useful for separating the built-up and non-built-up areas as well as vegetation and non-vegetation. The Unique identification (UID) technique used in estimating built-up and vegetation density from precisely classified images provided better and accurate assessment of built-up and vegetation density.  The results show that the built-up index involving PC2_NIR 1 and PC2_NIR 2 for the urban built land research achieved an optimal accuracy of 94, 43%. The best accuracy for vegetation data extraction was obtained from the combined NIR1_Red index with 99,51% and NIR2_Red values with an overall accuracy of 92,87%.   

scholarly journals Review Article: A comparison of flood and earthquake vulnerability assessment indicators

2017 ◽  
Vol 17 (7) ◽  
pp. 1231-1251 ◽  
Author(s):  
Marleen C. de Ruiter ◽  
Philip J. Ward ◽  
James E. Daniell ◽  
Jeroen C. J. H. Aerts
Keyword(s):  
Extensive Literature ◽  
Social Categories ◽  
Flood Vulnerability ◽  
Vulnerability Curve ◽  
Vulnerability Assessments ◽  
Object Based ◽  
Vulnerability Indicators ◽  
Earthquake Vulnerability ◽  
Physical Attributes ◽  
Selection Of

Abstract. In a cross-disciplinary study, we carried out an extensive literature review to increase understanding of vulnerability indicators used in the disciplines of earthquake- and flood vulnerability assessments. We provide insights into potential improvements in both fields by identifying and comparing quantitative vulnerability indicators grouped into physical and social categories. Next, a selection of index- and curve-based vulnerability models that use these indicators are described, comparing several characteristics such as temporal and spatial aspects. Earthquake vulnerability methods traditionally have a strong focus on object-based physical attributes used in vulnerability curve-based models, while flood vulnerability studies focus more on indicators applied to aggregated land-use classes in curve-based models. In assessing the differences and similarities between indicators used in earthquake and flood vulnerability models, we only include models that separately assess either of the two hazard types. Flood vulnerability studies could be improved using approaches from earthquake studies, such as developing object-based physical vulnerability curve assessments and incorporating time-of-the-day-based building occupation patterns. Likewise, earthquake assessments could learn from flood studies by refining their selection of social vulnerability indicators. Based on the lessons obtained in this study, we recommend future studies for exploring risk assessment methodologies across different hazard types.

scholarly journals Benthic habitat classification using multiscale GEOBIA on orthophoto images of Karimunjawa waters

2021 ◽  
Vol 6 (1) ◽  
pp. 55-59
Author(s):  
Yahya Dwikarsa ◽  
Abdul Basith
Keyword(s):  
Learning Algorithm ◽  
Support Vector ◽  
Benthic Habitat ◽  
Classification Algorithms ◽  
K Nearest Neighbors ◽  
Object Based ◽  
Svm Algorithm ◽  
Aerial Vehicle ◽  
Orthophoto Images ◽  
Selection Of

The scale value is an important part of the segmentation stage which is part of Object-Based Image Analysis (OBIA). Selection of scale value can determine the size of the object which affects the results of classification accuracy. In addition to setting the scale value (multiscale), selection of machine learning algorithm applied to classify shallow water benthic habitat objects can also determine the success of the classification. Combination of setting scale values and classification algorithms are aimed to get optimal results by examining classification accuracies. This study uses orthophoto images processed from Unmanned Aerial Vehicle (UAV) mission intended to capture benthic habitat in Karimunjawa waters. The classification algorithms used are Support Vector Machine (SVM), Bayes, and K-Nearest Neighbors (KNN). The results of the classification of combination are then tested for accuracy based on the sample and Training Test Area (TTA) masks. The result shows that SVM algorithm with scale of 300 produces the best level of accuracy. While the lowest accuracy is achieved by using SVM algorithm with scale of 100. The result shows that the optimal scale settings in segmenting objects sequentially are 300, 200, and 100

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