Feature extraction for object recognition based on visualcortex- like mechanisms

Li, Zhenghao; Zhou, Hu; Du, Xiuli; Fu, Shiqing

doi:10.1016/j.egypro.2011.11.503

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…After object description, its recognition can be modeled by using the extracted features [20,21]. For this purpose, in addition to simple cells, complex cells of the cortex must be modeled.…”

Section: Introductionmentioning

confidence: 99%

Object Recognition Inspiring HVS

Akbarpour

Mehrshad

Razavi

2018

IJEECS

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Human recognize objects in complex natural images very fast within a fraction of a second. Many computational object recognition models inspired from this powerful ability of human. The Human Visual System (HVS) recognizes object in several processing layers which we know them as hierarchically model. Due to amazing complexity of HVS and the connections in visual pathway, computational modeling of HVS directly from its physiology is not possible. So it considered as a some blocks and each block modeled separately. One models inspiring of HVS is HMAX which its main problem is selecting patches in random way. As HMAX is a hierarchical model, HMAX can enhanced with enhancing each layer separately. In this paper instead of random patch extraction, Desirable Patches for HMAX (DPHMAX) will extracted. HVS for extracting patch first selected patches with more information. For simulating this block patches with more variance will be selected. Then HVS will chose patches with more similarity in a class. For simulating this block one algorithm is used. For evaluating proposed method, Caltech 5 and Caltech101 datasets are used. Results show that the proposed method (DPMAX) provides a significant performance over HMAX and other models with the same framework.

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Section: Introductionmentioning

confidence: 99%

Object Recognition Inspiring HVS

Akbarpour

Mehrshad

Razavi

2018

IJEECS

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“…Vision system model of mammals mainly introduced by Hubel & Wiesel [2]. By studying neurons of vision, a strong theory of human vision with the name of scale-space theory have been proposed [3][4][5][6][7]. According to this theory, from the beginning to the end of the visual cortex with a family of local and scaled Gaussian operators will model.…”

Section: Introductionmentioning

confidence: 99%

Developing a Modified HMAX Model Based on Combined with the Visual Featured Model

Pourasad¹

2017

IJEECS

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Identify objects based on modeling the human visual system, as an effective method in intelligent identification, has attracted the attention of many researchers. Although the machines have high computational speed but are very weak as compared to humans in terms of diagnosis. Experience has shown that in many areas of image processing, algorithms that have biological backing had more simplicity and better performance. The human visual system, first select the main parts of the image which is provided by the visual featured model, then pays to object recognition which is a hierarchical operations according to this, HMAX model is also provided. HMAX object recognition model from the group of hierarchical models without feedback that its structure and parameters selected based on biological characteristics of the visual cortex. This model is a hierarchical model neural network with four layers, is composed of alternating layers that are simple and complex. Due to the high complexity of the human visual system is virtually impossible to replicate it. For each of the above, separate models have been proposed but in the human visual system, this operation is performed seamlessly, thus, by combining the principles of these models is expected to be closer to the human visual system and obtain a higher recognition rate. In this paper, we introduce an architecture to classify images based on a combination of previous work is based on the basic operation of the visual cortex. According to the results presented, the proposed model compared with the main HMAX model has a much higher recognition rate. Simulations was performed on the database of Caltech101.

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Feature extraction for object recognition based on visualcortex- like mechanisms

Cited by 2 publications

References 22 publications

Object Recognition Inspiring HVS

Object Recognition Inspiring HVS

Developing a Modified HMAX Model Based on Combined with the Visual Featured Model

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