Lateralization of object-shape information in semantic processing

Cognition ◽  
2004 ◽  
Vol 94 (2) ◽  
pp. B35-B43 ◽  
Author(s):  
R ZWAAN ◽  
R YAXLEY
2017 ◽  
Author(s):  
Le Chang ◽  
Pinglei Bao ◽  
Doris Y. Tsao

AbstractAn important question about color vision is: how does the brain represent the color of an object? The recent discovery of “color patches” in macaque inferotemporal (IT) cortex, the part of brain responsible for object recognition, makes this problem experimentally tractable. Here we record neurons in three color patches, middle color patch CLC (central lateral color patch), and two anterior color patches ALC (anterior lateral color patch) and AMC (anterior medial color patch), while presenting images of objects systematically varied in hue. We found that all three patches contain high concentrations of hue-selective cells, and the three patches use distinct computational strategies to represent colored objects: while all three patches multiplex hue and shape information, shape-invariant hue information is much stronger in anterior color patches ALC/AMC than CLC; furthermore, hue and object shape specifically for primate faces/bodies are over-represented in AMC but not in the other two patches.


2016 ◽  
Vol 10 (4) ◽  
pp. 327-338 ◽  
Author(s):  
Anwesha Khasnobish ◽  
Monalisa Pal ◽  
Dwaipayan Sardar ◽  
D. N. Tibarewala ◽  
Amit Konar

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Zheng Wang ◽  
Qingbiao Wu

Shape completion is an important task in the field of image processing. An alternative method is to capture the shape information and finish the completion by a generative model, such as Deep Boltzmann Machine. With its powerful ability to deal with the distribution of the shapes, it is quite easy to acquire the result by sampling from the model. In this paper, we make use of the hidden activation of the DBM and incorporate it with the convolutional shape features to fit a regression model. We compare the output of the regression model with the incomplete shape feature in order to set a proper and compact mask for sampling from the DBM. The experiment shows that our method can obtain realistic results without any prior information about the incomplete object shape.


Author(s):  
P. Sumathy ◽  
P. Shanmugavadivu ◽  
A. Vadivel

The object present in an image is an important content and can be used in CBIR applications. Identifying and representing the shape of the object is highly complex because there are uncertainties in the boundary of the object of interest. In this paper, the authors have proposed Fuzzy-Object-Shape to capture the shape of the object of interest along with the degree of impreciseness in the boundary information. The Fuzzy-Object-Shape information is extracted from each object in an image. This information provides a measure of closeness of the object of interest with well-known shapes. For each object, the fuzzy membership values are calculated and considered as feature vector. A similarity measure is proposed for measuring the degree of closeness of objects present in both query and database images. The performance of the proposed approach is compared with some of the recently proposed similar approaches. Benchmark dataset and uncontrolled dataset are used for the experiments and found that the performance of the proposed approach is encouraging.


Compared to color and texture, the shape is considered as an important feature for many real-time applications. In this chapter, Fuzzy Object Shape (FOS) is presented for extracting the shape information present in the images. It is further noticed that the boundary of the object is ill-defined and there is impreciseness and vagueness in the object information. The closeness of the object with well-known primitive shapes are estimated. It is known that the impreciseness can be effectively captured by fuzzy functions and FOS has offered seven fuzzy membership function for the same. The value of each fuzzy membership function are constructed as feature vector to define the properties of individual objects.


2003 ◽  
Vol 20 (3) ◽  
pp. 313-328 ◽  
Author(s):  
JAY HEGDÉ ◽  
DAVID C. VAN ESSEN

Contours and surface textures provide powerful cues used in image segmentation and the analysis of object shape. To learn more about how the visual system extracts and represents these visual cues, we studied the responses of V2 neurons in awake, fixating monkeys to complex contour stimuli (angles, intersections, arcs, and circles) and texture patterns such as non-Cartesian gratings, along with conventional bars and sinusoidal gratings. Substantial proportions of V2 cells conveyed information about many contour and texture characteristics associated with our stimuli, including shape, size, orientation, and spatial frequency. However, the cells differed considerably in terms of their degree of selectivity for the various stimulus characteristics. On average, V2 cells responded better to grating stimuli but were more selective for contour stimuli. Metric multidimensional scaling and principal components analysis showed that, as a population, V2 cells show strong correlations in how they respond to different stimulus types. The first two and five principal components accounted for 69% and 85% of the overall response variation, respectively, suggesting that the response correlations simplified the population representation of shape information with relatively little loss of information. Moreover, smaller random subsets of the population carried response correlation patterns very similar to the population as a whole, indicating that the response correlations were a widespread property of V2 cells. Thus, V2 cells extract information about a number of higher order shape cues related to contours and surface textures and about similarities among many of these shape cues. This may reflect an efficient strategy of representing cues for image segmentation and object shape using finite neuronal resources.


2011 ◽  
Vol 65 ◽  
pp. 491-496
Author(s):  
Yan Liu ◽  
Xiao Dong Shen

The purpose of this paper is to classify objects contained in images by the object categories. A new object feature in computer vision is introduced, that is fractional histograms of oriented gradients (fHoG). Due to the characteristics of fractional calculus, it is a descriptor that represents not only the object shape information but also the object texture details information. Together with pyramid decomposition, the fHoG features could be used to classify objects with similar shape but in different categories. For fHoG feature is some kind of local features, pyramid decomposition is designed to capture the hiding corresponding information between pixels. The two pyramid, spatial pyramid and Laplace pyramid, are both introduced. The former one is easy to compute while the calculation cost increasing fast as the pyramid level increasing. The latter one could save the calculation cost and get a better classification effect. Both of them could significantly improves the classification performance.


2013 ◽  
Vol 5 (4) ◽  
pp. 345-373 ◽  
Author(s):  
Manami Sato ◽  
Amy J. Schafer ◽  
Benjamin K. Bergen

AbstractWe report on two experiments that ask when and under what linguistic conditions comprehenders construct detailed shape representations of mentioned objects, and whether these can change over the course of a sentence when new information contradicts earlier expectations. We used Japanese because the verb-final word order of Japanese presented a revealing test case where information about objects can radically change with a subsequent verb. The results show that language understanders consistently generate a distinct and detailed shape for an object by integrating the semantic contributions of different sentential elements. These results first confirm that the tendency to generate specific shape information about objects that are involved in described events is not limited to English, but is also present in Japanese, a typologically and genetically distinct language. But more importantly, they shed light on the processing mechanism of object representation, showing that mental representations are initiated sentence medially, and are rapidly revised if followed by a verb that implies a change to an object shape. This work contributes to ongoing research on incremental language processing – comprehenders appear to construct extremely detailed semantic representations early in a sentence, and modify them as needed.


2020 ◽  
Author(s):  
Jiaming Hu ◽  
Xue Mei Song ◽  
Qiannan Wang ◽  
Anna Wang Roe

AbstractAn important aspect of visual object recognition is the ability to perceive object shape. How the brain encodes fundamental aspects of shape information remains poorly understood. Models of object shape representation describe a multi-stage process that includes encoding of contour orientation and curvature. While modules encoding contour orientation are well established (orientation domains in V1 and V2 visual cortical areas), whether there are modules for curvature is unknown. In this study, we identify a module for curvature representation in area V4 of monkey visual cortex and illustrate a systematic representation of low to high curvature and of curvature orientation, indicative of curvature hypercolumns in V4. We suggest that identifying systematic modular organizations at each stage of the visual cortical hierarchy signifies the key computations performed.SignificanceWe use intrinsic signal optical imaging in area V4 of anesthetized macaque monkey to study the functional organization of curvature representation. We find a modular basis for cue-invariant curvature representation in area V4 of monkey visual cortex and illustrate a systematic representation from low to high curvature and of curvature orientation, replete with curvature pinwheels. This is the first report of systematic functional organization for curvature representation in the visual system. The use of optical imaging has revealed at a population level spatial details of cortical responses, something which has not been evident from previous studies of single neurons. These data support a representational architecture underlying a ‘curvature hypercolumn’ in V4.


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