Theories of object-basedattention often make two assumptions: that attentional resources are facilitatory, and that they spread automatically within grouped objects. Consistent with this, ignored visual stimuli can be easier to process, or more distracting, when perceptually grouped with an attended target stimulus. But in past studies, the ignored stimuli often shared potentially relevant features or locations with the target. In this fMRI study, we measured the effects of attention and grouping on Blood Oxygenation Level Dependent (BOLD) responses in the human brain to entirely task-irrelevant events. Two checkerboards were displayed each in opposite hemifields, while participants responded to check-size changes in one pre-cued hemifield, which varied between blocks. Grouping (or segmentation) between hemifields was manipulated between blocks, using common (vs. distinct) motion cues. Task-irrelevant transient events were introduced by randomly changing the color of either checkerboard, attended or ignored, at unpredictable intervals. The above assumptions predict heightened BOLD signals for irrelevant events in attended vs. ignored hemifields for ungrouped contexts, but less such attentional modulation under grouping, due to automatic spreading of facilitation across hemifields. We found the opposite pattern, in primary visual cortex. For ungrouped stimuli, BOLD signals associated with task-irrelevant changes were lower, not higher, in the attended vs. ignored hemifield; furthermore, attentional modulation was not reduced but actually inverted under grouping, with higher signals for events in the attended vs. ignored hemifield. These results challenge two popular assumptions underlying object-basedattention. We consider a broader biased-competition framework: task-irrelevant stimuli are suppressed according to how strongly they compete with task-relevant stimuli, with intensified competition when the irrelevant features or locations comprise the same object.
In some respects, our method of extracting the approximate extent of an object bridges spatial attention with object-basedattention. Egly et al. (1994), for instance, report spreading of attention over an object. In their experiments, subjects detected invalidly cued targets faster if they appeared on the same object than if they appeared on a different object than the cue, although the distance between cue and target was the same in both cases. In our method, attention spreads over the extent of a proto-object as well, guided by the feature with the largest contribution to saliency at the attended location. Finding this most active feature is somewhat similar to the idea of flipping through an “object file”, a metaphor for a collection of properties that comprise an object (Kahneman and Treisman 1984). However, while Kahneman and Treisman (1984) consider spatial location of an object as another entry in the object file, in our implementation spatial location has a central role as an index for binding together the features belonging to a proto-object. Our method should be seen as an initial step toward a location invariant object representation, providing initial detection of proto-object that allow for subsequent tracking or recognition operations. In fact, in chapter 6, we demonstrate the suitability of our approach as a detection step for multi-target tracking in a machine vision application.
operations). They were then tested for object-based at- tention within algebraic expressions (e.g., w + a × c + f ). On each trial, two adjacent variables changed color, from black to either blue or red, and participants had to deter- mine whether these variables had the same color or dif- ferent color. If visual objects are constructed based on the expression’s hierarchical structure, then color verifi- cation should be facilitated when performed within an algebraic sub-expression (i.e., variables separated by multiplication), compared to when performed between sub-expressions (i.e., variables separated by addition). Moreover, this within-object advantage should occur only among those participants who have mastered the rules that generate the hierarchical structure of algebra. To investigate whether retraining the visual system mod- ulates algebraic performance, we also tested participants on a purely mathematical task: evaluating the algebraic equivalence of two expressions. If, after participants master the syntax of algebra, their visual system is retrained to play a functional role in algebraic reasoning, then object-basedattention for algebraic sub-expressions should improve performance in algebraic reasoning.
dependent motivations (strategy). Several studies have demonstrated the influence of these factors on visual attention. Adults responded with increased spatial attention to pictures depicting food stimuli relative to tools only after food and water deprivation (Mohanty, Gitelman, Small, & Mesulam, 2008). Arousing, erotic images rendered invisible with CFS can attract or repel observers’ attention, influenced by gender and sexual orientation (Jiang, Costello, Fang, Huang, & He, 2006). Learned associations also influence visual attention: Advantages in overcoming suppression induced by CFS have been demonstrated for Chinese vs. Hebrew characters for Chinese observers, and vice-‐ versa for Hebrew observers (Jiang, Costello, & He, 2007). In a study pairing biological reward with line gratings suppressed from awareness using CFS, individuals were more accurate in discriminating gratings previously paired with water rewards (even when “unseen”) (Seitz, Kim, & Watanabe, 2009). Thus, results from several veins of research implicate contributions of food, sex, learned associations, and strategy to stimulus prioritization. Finally, top-‐down strategies must be considered, as goal-‐directed selection induces substantial biases. A strong example of this is the observation that participants will “see” a face pattern in complete noise (Smith, Lestou, Gosselin, & Schyns, 2009). This perception may be induced by internal representations, driven by a search template mechanism in object-‐selective cortex. Evidence for this search
The domain-sensitivity reflected in the functional characteristics and neural distribution of the N250r component is consistent with the activation of cortical generators that operate as perceptual representation sub-systems, for example, a structural description system (SDS), word form descriptions, and face recognition units (Zhang et al., 1997; Martin-Loeches et al., 2005). Martin-Loeches et al. (2005) investigated ERP priming effects using either pictures of objects and faces or their names. The authors observed an enhanced occipital–temporal N250r (200–300 ms) for both face and object images, thought to reflect the comparison between structural representations with modality specific (pre-semantic) stored representations. Zhang et al. (1997) reported ERP repetition effects with a similar spatial and temporal distribution to the N250r as a visual memory potential (VMP; 220–260 ms post stimulus onset) with a right lateral posterior maximum enhanced for same-view repeated objects as compared with novel ones (Zhang et al., 1997). Zhang et al. (1997) propose that the VMP reflects the output of neural generators involved in a SDS and that these underlie the constancy of vision despite the infinite views that an object can input to the retinae (Zhang et al., 1997). Later ERP repetition effects reported as an N400 component are considered to reflect access to semantic and conceptual levels of object knowledge (Eddy et al., 2006).
Many objects typically occur in particular locations, and object words encode these spatial associations. We tested whether such object words (e.g, “head”, “foot”) orient attention toward the location where the denoted object typically occurs (i.e., up, down). Because object words elicit a perceptual simulation of the denoted object (i.e., the representations acquired during actual perception are reactivated), they were predicted to interfere with identification of an unrelated visual target subsequently presented in the object’s typical location. Consistent with this prediction, three experiments demonstrated that words denoting objects that typically occur high in the visual field hindered identification of targets appearing at the top of the display, whereas words denoting low objects hindered target identification at the bottom of the display. Thus, object words oriented attention to and activated a perceptual simulation in the object’s typical location. These results shed new light on how language affects perception.
569 unattended responses analogous to the ratio of ﬁ ring rates in these pre- 570 vious neurophysiological studies. Nevertheless, our results do provide 571 indirect evidence for the multiplicative gain as opposed to a mere base- 572 line shift hypothesis. Consider the result of increased attentional modu- 573 lation with object preference. A voxel's preference for a given object 574 may indicate that, for a ﬁ xed number of neurons tuned to different ob- 575 jects, the tuning curves of neurons are biased more towards the given 576 object than to the other objects. Alternatively, it may indicate that for 577 a ﬁ xed bias towards the given object an overall greater number of neu- 578 rons prefer the given object. Importantly, in both cases an unspeci ﬁ c 579 baseline shift would lead to an equal increase of neural activity for pre- 580 ferred and non-preferred objects, which is at odds with our results. To 581 illustrate why the increase in MI provides evidence for a multiplicative 582 gain mechanism as opposed to a pure baseline shift explanation, it is 583 helpful to consider two objects A and B and a hypothetical voxel 584 consisting of neurons with a preference for, e.g., object A. In case of a 585 pure baseline shift the voxel would show increased responses to both 586 objects and neural responses would therefore not become more infor- 587 mative about whether object A or B was presented. In contrast, in case 588 of multiplicative scaling, attention will lead to greater response ampli ﬁ - 589 cation for object A compared to object B, increasing the dynamic range 590 of responses and resulting in increased mutual information between 591 neural responses and presented objects. Thus, the increase in mutual in- 592 formation by attention provides a second line of evidence in favor of a 593 multiplicative gain mechanism and against a pure baseline shift 594 explanation.
A multi-features fusion tracking algorithm based on local kernels learning is proposed in . Histograms of multiple features are extracted based on the sub-image patches within the target region and the features fusion weights are calculated respectively for each patch according to the discrimination of features. A fast and yet stable model updation method is elaborated. The authors Hainan Zhao and Xuan Wang have also demonstrated how edge information can be merged into the mean shift framework without having to use a joint histogram. This is used for tracking objects of varying sizes.
is unclear. Different aspects of early visual function mature at different times and are probably related to dif- ferent underlying subcortical and cortical mechanisms. There are a few reasons why preterm infants and term infants might shift gaze to a particular object differently. Previous studies demonstrated that preterm children with intraventricular hemorrhage І-ІІ and BPD tended to have lower object permanence scores and shorter attention span [9, 32, 33]. Pel et al.  suggested that children born extremely preterm may have delays in response times to specific visual properties in processing visual information, suggesting deficits in neuronal connectivity in visual pathways at a microstructural level. The visual processing problem related to preterm birth might also influence our eye-tracking result, even though these infants had no ophthalmological impair- ments or structural brain damage on conventional MRI. None of the VLBW preterm infants in the present study had intraventricular hemorrhage ІІІ-ІV or periventricular leukomalacia, but 8 of the 26 preterm infants had moderate to severe BPD. However, logistic regression analysis revealed that the VLBW preterm infants’ attention performance was not associated with neonatal factors in clinically stable VLBW preterm infants due to a large number of covariates and small number of groups. Our data were not collected with a longitudinal design and the number of infants in each subgroup subdivided by postmenstrual age was too small to be amenable to statis- tical analysis. Additional studies with a larger cohort would help to better define early visual functioning in VLBW preterm infants.
highlighted attentive parts in the article for differ- ent models. The vanilla model just generates one sentence which only focuses on one part of article. The Micro DPPs model generates two sentences considering three parts of the article. Macro DPPs considered article spans that both vanilla model and Micro DPPs model paid attention to. We also checked the attention distribution of this sample. As shown in Figure 6, vanilla model (red) learned only several peaks over article position 70 to 90, which suggests that it only focuses on one sen- tence and repeats this sentence in a summary. At- tention learned by Micro DPPs model (green) still narrows to several peaks but explores more posi- tions compared to vanilla. Macro DPPs (blue) has more natural design of loss function and it opti- mize quality and diversity directly so it has a more scattering attention distribution.
Results: 16/6-Id injected mice were cognitively impaired as shown by significant differences in the preference for a new object in the novel object recognition test compared to controls (P = 0.012). Similarly, the preference for spatial novelty in the Y-maze test was significantly higher in the control group compared to the 16/6-Id-injected mice (42% vs. 9%, respectively, P = 0.065). Depression – like behavior and locomotor activity were not significantly different between the16/6-Id-injected and the control mice. Immunohistochemistry analysis revealed an increase in astrocytes and microglial activation in the hippocampus and amygdala, in the 16/6-Id injected group compared to the control. Conclusions: Passive transfer of 16/6-Id antibodies directly into mice brain resulted in cognitive impairments and histological evidence for brain inflammation. These findings shed additional light on the diverse mosaic
The spatially lateralized pattern of interference with two-item displays might be explained in terms of biased competition among visual inputs for limited processing capacity (Bundesen, 1990; Desimone & Duncan, 1995). In a non-competitive search situation, that is, when only a single item is presented in the display, there is no need for attention to be distributed. Accordingly, despite the well-documented attentional bias towards ipsilesional stimuli in extinction (e.g. Baylis & Driver, 1993; Humphreys, Romani, Olson, Riddoch, & Duncan, 1994), a left- or right-grouped nontarget would receive the full amount of available capacity, enabling a decision to be made between target presence and absence. However, distributing attention among multiple candidate target stimuli (in two-item displays) reduces the amount of attention that can be allocated to each single stimulus. In this situation, extinction patients allocate attentional weight predominantly to the right hemifield (Duncan et al., 1999), as a result of which target-nontarget similarity is primarily evaluated in the right (rather than the left) half of a given stimulus configuration. Due to this extinction- specific spatial attentional bias, right-grouped nontargets have a competitive advantage in the race for selection.
ABSTRACT: Along with the advancement in the technology, the data volume is also increasing and becoming huge day by day. And if the database is the image database, then the problem is more complex than expected and needs more of attention. In almost every field, the image databases are required, may be medicine, geographical systems, robotics, health sciences etc. So it plays a vital role in research and development areas. The main idea is extraction and discovery of new information or knowledge from the images present in the database. This extraction is known as Image Mining. It is more advanced field in data mining. It is different from data mining as it is focused on images and extraction of information from the images only. The relationship between the image sets and other patterns are mined based on the users requirements. Various algorithms have been developed and used for mining images but more work needs to be done, for the results to be more specific, precise, accurate and effective. This paper focuses on the current techniques and approaches for mining data from images and identifies the challenges and the future of the research in this area.
In an analysis pipeline sequential processing modules ob- tain and pass on their input as ObjectLayer instances containing all objects and the respective features. Discrete Voronoi tessellation  on the object layers map is used to compute the topological relationships. These are represented in an ObjectNetwork class which contains the ObjectNeighbourhood for each object. The neighbourhood information can be used, e.g. to compute the length of the border to an object of a certain class or the number of neighbouring objects (or touching objects, respectively) of a certain class. The object-based design makes it easier to communicate between algorithms and handle user interactions: instead of passing a label image (were often the meaning of the labels is implicit) and several lists/ dictionaries containing the related properties only an object layer or even a single object has to be passed. E.g. when the mouse is moved over a certain object the MouseEnterObject and MouseLeaveObject events are fired. The corresponding object is passed with the events data. Via this object reference all consumers of the event get access to all object properties and the corresponding object layer. The MouseEnterObject event, for example, is used to display the classification and features of this object (Figure 2A). Everything that happens is that in the event handler the name of the assigned class and the objects features are read from the corresponding ImageObject instance and printed to the popup. The object-based approach also allows for the (multi-) selection of objects in the image and scatter plot diagram. Selected objects are highlighted in both representations (Figure 2A and B). All algorithms can be used within the graphical user interface or in any other .NET application by referencing the CognitionMaster assemblies.
Previous research into object-based analysis and design for codesign has already made some progress though it is still in an early stage. Nam S. Woo has been working on the co-specification method for codesign [WDW94]. By using Object-Oriented Functional Specifications (OOFS) a system is divided into three groups: hardware, software, and codesign, which are then treated separately. Although OOFS for codesign group can been translated into C++ and Bestmap-C for the implementation of software and hardware respectively by the compilers, the estimation and evaluation of system performance have yet to be worked out. John Forrest has focused on heterogeneous specification and implementation-independent descriptions for codesign systems [For95]. The basic concept in his work is that a system is described as a set of concurrent modules, each module has a number of ports, and the associated module ports are connected. It reflects the common step of hierarchical decomposition. Two sets of notations, unbiased to hardware or software, have been proposed: an outline one and a reflection of part of it via C++. However, how the transition from these notations to the low-level implementation is smoothly carried out (< cosynthesis) and how the estimation and evaluation are integrated into this approach (<coestimation and coevaluation) remain unknown.
Armstrong et al.  demonstrated the use of object-based audio to provide three alter- nate soundtracks to the same TV programme. Churnside et al.  used an object-based approach to produce a responsive audio drama in which the narrative adapted to the ge- ographic location of the listener. In another production, Churnside  used objectbased audio to generate 2 broadcast quality mixes of a radio drama production of Pinnochio. Armstrong et al.  demonstrated the use of object-based broadcasting in the production of a variable length radio documentary. Each of these productions has demonstrated vari- ous benefits and challenges associated with object-based production; however, in each case the object-based aspects of the production have been additional to a traditional stereo or surround production meaning that, necessarily, compromises were made.
Degeneracy Problem: It is a hypothesis tracking method which calculates the posterior distribution using a set of weighted particles. Particles are weighted based on the likelihood and weight of each particle is updates based on the data associated and the observations from the current image frame. Weight disparity which leads to the weight collapse is a main problem encountered in the particle filter which can be solved using the resampling before the weights collapse. This problem is called degeneracy problem. In the resampling technique the particles with the minimum weights are discarded and new particles with higher weight in the likelihood are generated. The performance of the object tracking algorithm can be also improved by the proper choice of resampling method. Most common sampling techniques are importance sampling, stratified sampling, sequential importance resampling etc.
Dehghani et al. (2019) proposed Universal Trans- former for solving the problems of Transformer including the weakness for long distance depen- dency. Although it has a mechanism to repeat up- dating the states for each word with parameters shared, it requires a larger number of parameters than Transformer. There could be an approach like BERT (Devlin et al., 2019) where the number of parameters is increased significantly to make a more powerful Transformer model. Our approach, on the other hand, improves the strength of RNN with a little increase of parameters as shown in Ta- ble 5. Moreover, Iida et al. (2019) also applied the multi-hop attention mechanism to the Transformer and reported that the Transformer augmented with the multi-hop attention mechanism significantly outperformed the Transformer. Among other ex- isting approaches to neural machine translation, it is known that ConvS2S (Gehring et al., 2017) is equipped with multiple decoder layers where each decoder layer has a separate attention module. The attention of each of those multiple layers is com- puted and is then fed to another layer, which then takes the fed information into account when com- puting its own attention etc. The way those multi- ple attentions are computed is similar to the multi- head and multi-hop attention mechanism proposed in this paper.
In experiments, we apply our system to a Chinese-to-Japanese translation task of scientific text. Our experimental results show that the attention-based unknown word replacement method consistently im- proves the BLEU scores by about 1.0 for the baseline system, the domain adaptation system, and the ensemble of the two systems. Moreover, our manual analysis on the replaced unknown words indicates that the scores can be further improved if a high quality dictionary is available. While the domain adap- tation method does not improve upon the baseline system in terms of the automatic evaluation metrics, the ensemble of the systems with and without the domain adaptation method boosts the BLEU score by 2.7. As a result, our UT-KAY system has been selected as one of the top three systems in the Chinese- to-Japanese task at WAT 2016.
The object tracking algorithms based on mean shift are good and efficient. But they have limitations like inaccuracy of target localization and sometimes complete tracking failure. These difficulties arises because of the fact that in basic kernel bas ed mean shift tracking algorithm, the centroid is not always at the centre of the target and the size of tracking window remains constant even if there is a major change in the size of object. It causes introduction of large number of background pixels in the object model which give localization errors or complete tracking failure. To deal with these challenges a new robust tracking algorithm based on edge based centroid calculation and automatic kernel bandwidth selection is proposed in this paper. This approach includes relocation of the track window on the middle of the target object in every frame and automatic size adjustment of tracking window so that minimum background pixels will be introduced in object model. The proposed algorithm show good results for almost all the tracking challenges faced by basic mean shift kernel tracking method.