Audio object coding based on N-step residual compensating

Author(s):  
Chenhao Hu ◽  
Xiaochen Wang ◽  
Ruimin Hu ◽  
Yulin Wu
Keyword(s):  
GeoJournal ◽  
1990 ◽  
Vol 20 (2) ◽  
Author(s):  
Heiner Benking ◽  
Heiko Schmidt v. Braun

Author(s):  
Sung Soo Hwang ◽  
Sujung Kim ◽  
Seong-Dae Kim ◽  
Sang-Young Park
Keyword(s):  

Author(s):  
Chenhao Hu ◽  
Ruimin Hu ◽  
Xiaochen Wang ◽  
Tingzhao Wu ◽  
Dengshi Li
Keyword(s):  

2017 ◽  
Author(s):  
Daniel Kaiser ◽  
Marius V. Peelen

AbstractTo optimize processing, the human visual system utilizes regularities present in naturalistic visual input. One of these regularities is the relative position of objects in a scene (e.g., a sofa in front of a television), with behavioral research showing that regularly positioned objects are easier to perceive and to remember. Here we use fMRI to test how positional regularities are encoded in the visual system. Participants viewed pairs of objects that formed minimalistic two-object scenes (e.g., a “living room” consisting of a sofa and television) presented in their regularly experienced spatial arrangement or in an irregular arrangement (with interchanged positions). Additionally, single objects were presented centrally and in isolation. Multi-voxel activity patterns evoked by the object pairs were modeled as the average of the response patterns evoked by the two single objects forming the pair. In two experiments, this approximation in object-selective cortex was significantly less accurate for the regularly than the irregularly positioned pairs, indicating integration of individual object representations. More detailed analysis revealed a transition from independent to integrative coding along the posterior-anterior axis of the visual cortex, with the independent component (but not the integrative component) being almost perfectly predicted by object selectivity across the visual hierarchy. These results reveal a transitional stage between individual object and multi-object coding in visual cortex, providing a possible neural correlate of efficient processing of regularly positioned objects in natural scenes.


Author(s):  
Emmanouil Froudarakis ◽  
Uri Cohen ◽  
Maria Diamantaki ◽  
Edgar Y. Walker ◽  
Jacob Reimer ◽  
...  

AbstractDespite variations in appearance we robustly recognize objects. Neuronal populations responding to objects presented under varying conditions form object manifolds and hierarchically organized visual areas are thought to untangle pixel intensities into linearly decodable object representations. However, the associated changes in the geometry of object manifolds along the cortex remain unknown. Using home cage training we showed that mice are capable of invariant object recognition. We simultaneously recorded the responses of thousands of neurons to measure the information about object identity available across the visual cortex and found that lateral visual areas LM, LI and AL carry more linearly decodable object identity information compared to other visual areas. We applied the theory of linear separability of manifolds, and found that the increase in classification capacity is associated with a decrease in the dimension and radius of the object manifold, identifying features of the population code that enable invariant object coding.


Author(s):  
Yulin Wu ◽  
Ruimin Hu ◽  
Xiaochen Wang ◽  
Chenhao Hu ◽  
Gang Li

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