scholarly journals Image- and Task-Based Contributions to Human Object Localization in Natural Scenes

2021 ◽  
Vol 21 (9) ◽  
pp. 2851
Author(s):  
Colin S. Flowers ◽  
Mary A. Peterson
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anna Kosovicheva ◽  
Peter J. Bex

AbstractWe effortlessly interact with objects in our environment, but how do we know where something is? An object’s apparent position does not simply correspond to its retinotopic location but is influenced by its surrounding context. In the natural environment, this context is highly complex, and little is known about how visual information in a scene influences the apparent location of the objects within it. We measured the influence of local image statistics (luminance, edges, object boundaries, and saliency) on the reported location of a brief target superimposed on images of natural scenes. For each image statistic, we calculated the difference between the image value at the physical center of the target and the value at its reported center, using observers’ cursor responses, and averaged the resulting values across all trials. To isolate image-specific effects, difference scores were compared to a randomly-permuted null distribution that accounted for any response biases. The observed difference scores indicated that responses were significantly biased toward darker regions, luminance edges, object boundaries, and areas of high saliency, with relatively low shared variance among these measures. In addition, we show that the same image statistics were associated with observers’ saccade errors, despite large differences in response time, and that some effects persisted when high-level scene processing was disrupted by 180° rotations and color negatives of the originals. Together, these results provide evidence for landmark effects within natural images, in which feature location reports are pulled toward low- and high-level informative content in the scene.


Author(s):  
James W. Meehan ◽  
Thomas J. Triggs

The size-distance invariance hypothesis suggests that the perceived size and the perceived distance of objects in a field viewed naturally are closely related. However, this relationship breaks down when scenes are viewed through high-power optical systems. When natural scenes are viewed through an imaging display of unity magnification, there is a reduction in their apparent size. This raises the question of whether the relationship breaks down when scenes are viewed through a low-power imaging display. A single-lens reflex camera was used as an imaging display that enabled subjects to vary the size of imaged real-world scenes. Judgments of size were found to vary with depth information in scenes and between monocular and binocular viewing, consistent with a previous finding, but judgments of distance did not vary significantly across either of these conditions. The results suggest that judgments of size and judgments of distance with imaging displays are not influenced uniformly by environmental and task variables.


2018 ◽  
Vol 18 (10) ◽  
pp. 393
Author(s):  
Colin Flowers ◽  
Mary Peterson

2020 ◽  
Vol 20 (11) ◽  
pp. 281
Author(s):  
Tiasha Saha Roy ◽  
Arpita Saha Chowdhury ◽  
Sucheta Chakravarty ◽  
Koel Das

2019 ◽  
Vol 19 (10) ◽  
pp. 58a
Author(s):  
Colin S Flowers ◽  
Rachel M Skocypec ◽  
Mary A Peterson

2018 ◽  
Author(s):  
Murat Demirtaş ◽  
Adrian Ponce-Alvarez ◽  
Matthieu Gilson ◽  
Patric Hagmann ◽  
Dante Mantini ◽  
...  

AbstractA fundamental question in systems neuroscience is how spontaneous activity at rest is reorganized during task performance. Recent studies suggest a strong relationship between resting and task FC. Furthermore, the relationship between resting and task FC has been shown to reflect individual differences. Particularly, various studies have demonstrated that the FC has higher reliability and provides enhanced detection of individual differences while viewing natural scenes. Although the large-scale organization of FC during rest and movie-viewing conditions have been well studied in relation to individual variations, the re-organization of FC during viewing natural scenes have not been studied in depth. In this study, we used principal component analysis on FC during rest and movie-viewing condition to characterize the dimensionality of FC patterns across conditions and subjects. We found that the variations in FC patterns related to viewing natural scenes can be explained by a single component, which enables identification of the task over subjects with 100% accuracy. We showed that the FC mode associated to viewing natural scenes better reflects individual variations. Furthermore, we investigated the signatures of movie-viewing-specific functional modes in dynamic FC based on phase-locking values between brain regions. We found that the movie-specific functional mode is persistent across time; suggesting the emergence of a stable processing mode. To explain the reorganization of whole-brain FC through the changes in local dynamics, we appeal to a large-scale computational model. This modelling suggested that the reorganization of whole-brain FC is associated to the interaction between frontal-parietal and frontal-temporal activation patterns.


Author(s):  
Sandipan Choudhuri ◽  
Nibaran Das ◽  
Ritesh Sarkhel ◽  
Mita Nasipuri

Object localization is one of the inherent tasks of computer vision. It plays an intrinsic role in object detection tasks that initiate with a recognition procedure of figuring out the presence of single/multiple instances of objects of interest in a given image. It involves determination of precise locations of object instances. This paper presents an overview of some of the popularly used approaches to the object localization problem, involving efficient branch-and-bound strategy for sub-window search, super-pixel neighborhood information based approach, boosted local structured Histogram of Oriented Gradients-Local Binary Patterns (HOG-LBP) based strategy, multi-instance learning based weakly supervised object localization, object localization by utilizing deep networks and image tag based object localization. The performance of the mentioned approaches have been compared on the basis of their results on PASCAL-VOC 2007 dataset.


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