Study of Information Processing Systems Based on Neural Network of Visual Cortex For
analysis of Astronomical and Scientific data
ナノシステム研究科 孫哲（Sun Zhe）
指導教員

Ruggero Micheletto

1.General Background Information
With the recent convergence of multi-disciplinary sciences and the necessity of big
data processing, the study of the Brain is playing an increasingly important role.
Human beings are borne to have the ability to recognize things around, and thissystem is working every second to help us deal with things we encounter in daily life.
In fact, all Lens and retina are doing is almost like a CCD of non-professional
cameras. However, so far, human being still can't even design one camera that canmatch with this Object Recognition ability, this is due to the fact that we are still far
from grasp all the details of the brain functioning. Although we do have gain lots ofimprovement and achievement in General Object Recognition techniquesdevelopment, we haven't yet make any breakthrough. Therefore, it have significant
meaning to improve our understanding of General Object Recognition by looking into
visual stream, whatever it is from theoretical or practical aspect.
For this purpose in this study, we use the computational model of object recognitionin the brain cortex (HMAX) to distinguish objects. In particular, we have developed asystem able to recognize astronomical object in an astronomical database of data.
The HMAX model was proposed by T. Poggio in MIT, it is a feed-forward hierarchicalmodel and extend the classical simple-to-complex cells model. Using bottom up
process can speed calculation and promote the general recognition of patterns in acomplex database. And in this study, we used support vector machine (SVM) as
classifiers. The Sloan Digital Sky Survey (SDSS) is exploited to test our approachwith astronomical data.
2.Research Method
2.1Ventral Stream and Hmax model

Figure 1. The ventral steam for pattern recognition in our brain
As we know, the visual information work-flow in our brain start from V1, V2, V4 to
AIT. Hmax model is a mathematical representation of information processing in the
Brain. It contains S1 Layer, C1 layer, S2 Layer, C2 Layer. The S1, S2 layer work likethe simple cells and C1, C2 layer was used as the complex cells in visual stream.

2.2 S1 Layer and C1 Layer
We have an initial input image of 256x256 pixels, representing astronomical data, as
for example Figure 1:

Figure 2. Images from SDSS database
The S1 Layer is applied on 12 resized versions of this image at different scales(256x256, 214x214, 180x180 down to 38x38 pixels). In higher mammals brain,
images are filtered along orientations. A Gabor filter is used to reproduce thisfunctionality. In our HMAX model each scale the Gabor filter is applied at 12 different
orientations, for a total of 12x12 set of output data (12 scales by 12 orientations).
The Gabor filters are described by:( X +γ 2 Y 2 )2π
G( x , y)=exp(−)cos( λ )2
2σWhere X =cos θ− ysin θ and Y =sin θ+ ycos θ .We adjusted the same filter
parameters for all of the scales,orientation θ ,and wavelength λ .
After the S1 layer is processed, the 12x12 group of data are received by the C1 layer.
In C1, a major rescaling is performed as happens in the hierarchical information flowin the mammal brain. In our algorithm each resized group is compared with theadjacent smallest sized group of data (for example 256x256 compared with
214x214). The comparison is made with normalized XY coordinates on a 3x3 grid.
The biggest data value is selected and a single data point is generated. All the image
is scanned and a new smaller group of data is generated (for example, 47x47). There
are 12 orientations for each group size, so 12 of these resized groups are generated
this way. The process continues with all the other sizes (214x214 compared with
180x180 and so on) until a new set of smaller data groups are created. The groups
are these sizes (47x47, 39x39, 33x33 down to 5x5). Finally, because of thiscomparison in couples, one of the sizes is lost and after C1 operations, we have 11
sizes , and 12 orientation, for a total of 11x12=132 groups. This elaboration is done toremove noise in scale-independent pattern recognition processes observed in
mammal.
2.1. Layers S2 and C2
In S2 layer, we selected 4075 patches of data. The size of these patches is 4x4x12 or
8x8x12 or 16x16x12 chosen at random and placed at random position. The 12

appears because we have 12 orientations. Because of normalization of coordinates,
these patches have the same relative area on each scale. A Gaussian filter of thiskind is applied:( X i −P j )2
R( X i , P j )=exp (−)
2 σ2 αwhere X i is a three-dimensional vector representing the data anddimensional patch.

P j is the three

The output of this layer is a new group of data of size AxAx4075, where A is theoriginal size of the group. The 4075 points are the maximum value of the Gaussian
filtering above. After this stage we have 11 group of data, each of them AxAx4075 insize, where A is the original group size (47x47, 39x39, 33x33 down to 5x5 as above).
The C2 layer:
These 11 groups are fed to the C2 layer that in the same fashion as C1 layer, it
compare different patches on a 3x3 grid. This time the comparison is not made on
two adjacent sizes, but is done on 6 of them. For example the group A=47 iscompared with A=39, A=33 and so on. A single maximum value is determined. This
process is repeated another group of 6 different sizes. At the end two group of data ofsize A=39 and A=21 is generated. Each group of data has size AxAx4075.
Now spatial dependence is removed. For each AxA sheet data in each group, themaximum value is taken. So each AxAx4075 group is reduced to a one-dimensional
vector of size 4075. We do that on the two groups and join the results. So the output
of C1 is a single one-dimensional vector of size 8150. This final vector represent thegeneralization of the original image features, and we call it from now on “the features”
embedded in the image.
2.2. Support Vector Machine For Classification
Before the classification, the data should be normalized as preprocessing.

Figure 3. An example for classification
SVM is a classifier for supervised learning which build classification model by
analyzing training data. In our case, we used SVM with RBF (Radial Basis function)
kernel Model for the classification. We used an external python library called scikitlearn to implement SVM. Scikit-learn harnesses this rich environment to provide stateof the art implementations of many well known machine learning algorithms. And it
maintains an easy to use interface tightly integrated with python language.

3.Result and Discussion
3.1 The SDSS database
We used the Solar Digital Sky Survey（SDSS）Data Release 8 as our dataset. In
this release, all of the imaging data was taken by the SDSS imaging camera whichcontains totally over 14000 square degree of sky.
In this study , we prepared the dataset with about 500 stars and 500 galaxies forsample training. And we used about 1000 images of objects for prediction.
3.2 Result and Prospect
In this research, we have used Hmax Model to carry Astronomical data andclassification. The identifiability have been improved above 90% and in the process,we have simulated the information delivering method of biological brain and achieved
effective features. However, in terms of mechanical learning, we have applied
traditional SVM. We hope we are able to obtain the category algorithm of brain bymeans of further studies of brain.
Reference
1.Y. D. Tian, L. Q. Zhang.Relevant Algorithms for General Object Recognition Basedon Feature Combination.Shanghai Jiao Tong University Journal,2007.
2.J. Mutch and D. G. Lowe.Object class recognition and localization using sparsefeatures with limited receptive fields. International Journal of Computer Vision. Vol. 56
3.T. Serre, L. Wolf, and T. Poggio. Object recognition with features inspired by visualcortex. In CVPR, San Diego, June 2005.
4.C. Elting, C. A. L. Bailer-Jones, K. W. Smith. Photometric Classification of Stars,
Galaxies and Quasars in the Sloan Digital Sky Survey DR6 Using Support Vector
Machines. American Institute of Physics Conference Series. Vol. 1082. pp. 9-14 ,
2008
研究概要

宇宙電波望遠鏡の観測データは膨大なため、従来の研究には、単純な数学モデルを利用しているものの、認識の精度は低く、研究者の経験に依存する部分が大きい。
これを改善するために、博士後期には、「視覚野神経回路網モデルを用いた情報処理システムの研究」というテーマで研究を展開したい。人間の視覚野神経回路網情報処理モデルと機械学習技術を利用して、観測データを自動的にパターン認識すると分析できるシステムを開発する。「視覚野神経回路網モデル」（HMAX モデル）とは、生理学的実験により判明した、大脳視覚野における情報処理方法を利用し、データの特徴抽出を行うモデルである。具体的には、 V １野と V ２野の特徴抽出細胞および V4 野を経て IT 野に至るという階層的な処理である。

Study of Information Processing Systems Based on Neural Network of
Visual Cortex For Analysis of Astronomical and Scientific Data

平成 26 年度

修士論文

横浜市立大学大学院
生命ナノシステム研究科
ナノシステム科学
Sun Zhe1

専攻

Contents
Abstract・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・5
1. Introduction・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・10
1.1. Research Background・・・・・・・・・・・・・・・・・・・・・・・10
1.2 Research purpose・・・・・・・・・・・・・・・・・・・・・・・・・11
1.3 Research Method ・・・・・・・・・・・・・・・・・・・・・・・・・11
2.Neural Networks and Artificial Neural Networks・・・・・・・・・・・・・・13
2.1 Intelligence and machine learning・・・・・・・・・・・・・・・・・・13
2.2 The Visual Cortex Structure and Pattern Recognition・・・・・・・・・・16
3.Standard HMAX model ・・・・・・・・・・・・・・・・・・・・・・・・・22
3.1 General methods(HOG, SIFT) for feature descriptors in computer vision ・・ 20
3.2 Standard HMAX model ・・・・・・・・・・・・・・・・・・・・・・・21
3.3 Support Vector Machine ・・・・・・・・・・・・・・・・・・・・・・26
4.System for astronomical data analysis ・・・・・・・・・・・・・・・・・・29
4.1 SDSS database ・・・・・・・・・・・・・・・・・・・・・・・・・・29
4.2 Astronomical data Features ・・・・・・・・・・・・・・・・・・・・・30
4.2.1 Initial processing ・・・・・・・・・・・・・・・・・・・・・・・・30
4.2.2 Process in S1 Layer ・・・・・・・・・・・・・・・・・・・・・・31
4.2.3 Process in C1 Layer ・・・・・・・・・・・・・・・・・・・・・・32
4.2.4 Process in S2 Layer ・・・・・・・・・・・・・・・・・・・・・・33
4.2.5 Process in C2 Layer ・・・・・・・・・・・・・・・・・・・・・・34
4.3 Data Training And Prediction・・・・・・・・・・・・・・・・・・・・・34

2

5. Dataset Prediction・・・・・・・・・・・・・・・・・・・・・・・・・・37
5.1 Test result of Prediction・・・・・・・・・・・・・・・・・・・・・・37
5.2 Conclusion and Prospect・・・・・・・・・・・・・・・・・・・・・37
5.3 Research In the feature ・・・・・・・・・・・・・・・・・・・・・38
Reference ・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・40
Japanese Abstract ・・・・・・・・・・・・・・・・・・・・・・・・・・・42
Acknowledgements ・・・・・・・・・・・・・・・・・・・・・・・・・・・43
Appendices ・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・44

3

Study of Information Processing Systems Based on Neural Network of
Visual Cortex For Analysis of Astronomical and Scientific Data

Sun Zhe
主指導員

Ruggero Micheletto

副指導員

木下

副指導員

大月 俊也

副指導員

郁雄

Robert Kanaly

4

Study of Information Processing Systems Based on Neural Network of Visual Cortex For
analysis of Astronomical and Scientific data
ナノシステム研究科 孫哲（Sun Zhe）
指導教員

Ruggero Micheletto

1.General Background Information
With the recent convergence of multi-disciplinary sciences and the necessity of big
data processing, the study of the Brain is playing an increasingly important role.
Human beings are borne to have the ability to recognize things around, and thissystem is working every second to help us deal with things we encounter in daily life.
In fact, eye lens and retina work almost like a CCD of non-professional cameras.
However, so far, human being still can't even design one camera that can match withthe eye and brain object recognition ability, this is due to the fact that we are still far
from grasping all the details of the brain functioning. Although we do have done of
improvement and achievement in General Object Recognition techniquesdevelopment, we haven't yet make any breakthrough. Therefore, it have significant
meaning to improve our understanding of General Object Recognition by looking into
visual stream, whatever it is from theoretical or practical aspect.
For this purpose in this study, we use our original version of the computational modelof object recognition in the brain cortex (HMAX) to distinguish objects. In particular,
we have developed a system able to recognize astronomical object in an
astronomical database of data. The HMAX model was proposed by T. Poggio in MIT,
it is a feed-forward hierarchical model and extend the classical simple-to-complex
cells model. Using bottom up process can speed calculation and promote the general
recognition of patterns in a complex database. And in this study, we used support
vector machine (SVM) as classifiers. The Sloan Digital Sky Survey (SDSS) is
exploited to test our approach with astronomical data.
2.Research Method
2.1Ventral Stream and Hmax model

Figure 1. The ventral steam for pattern recognition in our brain
As we know, the visual information work-flow in our brain start from V1, V2, V4 to
AIT. Hmax model is a mathematical representation of information processing in the
Brain. It contains S1 Layer, C1 layer, S2 Layer, C2 Layer. The S1, S2 layer work likethe simple cells and C1, C2 layer was used as the complex cells in visual stream.
2.2 S1 Layer and C1 Layer
5

We have an initial input image of 256x256 pixels, representing astronomical data, as
for example Figure 1:

Figure 2. Images from SDSS database
The S1 Layer is applied on 12 resized versions of this image at different scales(256x256, 214x214, 180x180 down to 38x38 pixels). In higher mammals brain,
images are filtered along orientations. In the same fashion, a Gabor filter is used toreproduce this functionality. In our improved HMAX model, each scale the Gabor filter
is applied at 12 different orientations, for a total of 12x12 set of output data (12 scalesby 12 orientations).
The Gabor filters are described by:G( x , y)=exp(−( X +γ 2 Y 2 )2π)cos( λ )
2
2σ

(1)

Where θ is the orientation, X =cos θ− ysin θ and Y =sin θ+ ycos θ . We used thesame filter parameters for all of the scales, orientations θ , and wavelength λ .
The C1 layer :After the S1 layer is processed, the 12x12 group of data are received by the C1 layer.
In C1, a major rescaling is performed as happens in the hierarchical information flowin the mammal brain. In our algorithm each resized group is compared with theadjacent smallest sized group of data (for example 256x256 compared with
214x214). The comparison is made with normalized XY coordinates on a 3x3 grid.
The biggest data value is selected and a single data point is generated. All the image
is scanned and a new smaller group of data is generated. There are 12 orientations
for each group size, so 12 of these resized groups are generated this way. The
process continues with all the other sizes (214x214 compared with 180x180 and soon) until a new set of smaller data groups are created. Finally, because of thiscomparison in couples, one of the sizes is lost and after C1 operations, we have 11
sizes, and 12 orientation, for a total of 11x12=132 groups. This elaboration is done toremove noise in scale-independent pattern recognition processes, as observed in
mammal.
2.3 Layers S2 and C2
6

In S2 layer, a maximization process is done along all the 12 orientations. We
selected 4075 random patches of data for each group. The size of these patches is
4x4x12 or 8x8x12 or 16x16x12 chosen at random and placed at random positions.
The 12 appears because we have 12 orientations. Because of normalization of
coordinates, these patches have the same relative area on each scale. A Gaussian
filter of this kind is applied:R( X i , P j )=exp (−( X i −P j )2)
2 σ2 α(2)where X i is a three-dimensional vector representing the data and P j is the three
dimensional patch.
The output of this layer is a new group of data of size A× A×4075 , where A is theoriginal size of the group. The 4075 points are the maximum value of the Gaussian
filtering above. After this stage we have 11 group of data, each of them
A× A×4075 in size, where A is the original group size (214x214, 180x180 …
47x47, 39x39, 33x33 down to 5x5 as in table 1).
Input Layer

S1 Layer

C1 Layer

S2 Layer

C2 Layer

256×256246×246

47×47

44×44

1×1

214×214204×204

39×39

36×36

180×180

170×170

33×33

30×30

152×152

142×142

27×27

24×24

128×128

118×118

21×21

18×18

106×106

96×96

17×17

14×14

90×90

80×80

15×15
Table 1

12×12

1×1

The C2 layer:
These 11 groups are fed to the C2 layer that in the same fashion as C1 layer, it
compare different patches on a 3x3 grid. This time the comparison is not made on
two adjacent sizes, but is done on 6 of them. For example the patches of group A=47
are compared with A=39, then A=33 and so on. A single maximum value isdetermined. This process is repeated another group of 6 different sizes. At the end
two group of data of size A=39 and A=21 are generated. Each group of data has size
A× A×4075 .
Now spatial dependence is removed. For each AxA sheet data in each group, themaximum value is taken. So each A× A×4075 group is reduced to a
one-dimensional vector of size 4075. We do that on the two groups and join the
results. So the output of C1 is a single one-dimensional vector of size 8150. This final
vector represent the generalization of the original image features, and we call it fromnow on “the features” embedded in the image.
7

2.4 Support Vector Machine For Classification
Before the classification, the data should be preprocessed and normalized.

Figure 3. An example for classification
SVM is a classifier for supervised learning which build classification model by
analyzing training data. In our case, we used SVM with RBF (Radial Basis function)
kernel Model for the classification. We used an external python library calledscikit-learn to implement SVM.
3.Result and Discussion
3.1 The SDSS database
We used the Solar Digital Sky Survey（SDSS）Data Release 8 as our dataset. In
this release, all of the imaging data was taken by the SDSS imaging camera whichcontains totally over 14000 square degree of sky.
In this study , we prepared the dataset with about 200 stars and 200 galaxies forsample training. And we used about 1000 images of objects for prediction.
3.2 Result and Prospect
In this research, we have used Hmax Model to carry Astronomical data andclassification. The accuracy of detection have been improved to 80%, and in theprocess, we have simulated the information delivering method of biological brain andachieved effective features. However, in terms of mechanical learning, we haveapplied traditional SVM. We hope we are able to obtain the category algorithm ofbrain by means of further studies of brain.

8

研究概要（日本語）
視覚野ニューロンネットワーク情報処理に基づいた、天文データの識別システムの研究

研究概要

人間の視覚野は、目から得たデータを処理し、学習と記憶に基づき、パターン認識をしている。脳の働き方を模倣した、学習機能を持つアルゴリズムは、一般的なデータパターン（実験データ等）の認識率が高い。従来の研究では、輝度の勾配方向と勾配強度を利用しているが、認識の精度は低い。
これは、宇宙電波望遠鏡の観測データはノイズが多く複雑かつ、情報量が膨大なためである。これを改善するために、人間の視覚野神経回路網情報処理モデルと機械学習技術を利用して、観測データを精度良くパターン認識できるシステムを開発する。本研究では、MIT の Poggio 教授によって発明された HMAX モデルを用いた。HMAX モデルは、生理学的実験に基づいた大脳視覚野の情報処理方法を利用し、データの特徴抽出を行うモデルである。具体的には、 V １野と V ２野の特徴抽出細胞
および V4 野を経て IT 野に至る階層的な処理である。このモデルを利用し、視覚野単純型細胞と複雑型細胞の情報処理プロセスを次のような４層で行う。まず、S1 層で処理データと各方向のガボー
ルフィルタを畳み込み計算を行って、次に S2 層で S1 層の直近データを統合する。そして、S2 層でデータパッチを出力し、C2 層で全部のデータを統合する。最後に、C2 層処理後のデータは、サポートベクターマシン（SVM）でパターンの学習と予測を行う。

9

1. Introduction
1.1. Research Background
The importance of sensory and communicational functions comes from the fact thatthe human body is an open biosystem , in a permanent exchange of energy,
substances and information within its surrounding environment. The human bodyreceives

information from theEnvironment around it: 1% by taste, 1 to 5% bytouch, 3 to 5% by smell, 11% by hearing and 83% is by sight. And the vision ofhuman outperforms machine vision systems with respect to almost any measure, andemulating the information system in visual cortex has always been an attractive
research topic.

The vision plays an important role in all of the perceptions. Comparing with the otherperception information systems, the visual system is also the most complex. The
computer can realize the capability of human visual recognition by researching theinformation system in ventral stream, and clarifying the mechanisms of its cognition
and target recognition, which is significant to carry out the pattern recognition for avariety of data of the research, life and industry [1].

The biological vision systems has a completely different mechanism comparing to
the image processing system of modern computer, but its capacity of the perception,
cognition and the target recognition and the extremely strong ability to adapt thecomplex environment, all of which the computer system still can’t match, evennowadays with the highly developed computing technology.

10Under the suggestion of prof. T. Poggio, professor at MIT, we developed an improved
implementation of HMAX model, written in python interpreted language for the
purpose of obtaining a pattern recognition method which is much more similar to thatof human brain. Besides, as a joint study with JAXA, we used the improved system toprocess astronomical data to test the effectiveness of our new model. Final goal is touse actual JAXA data for automatic recognition of celestial structures.

1.2 Research purpose
The dataset from the CCD photometric system of Sloan Digital Sky Survey used six
sets of CCD to measure the five wave brands (u,g,r,i) of celestial bodies
simultaneously and the photometric parameters include color, outline and size. SDSS
could obtain the photometric data of more than 100 million celestial bodies. In thisstudy, we studied, identified and classified the stars and the galaxies in the SDSS of
the database by using the HMAX model.

Fig 1-1 The star and galaxy image from SDSS database

1.3 Research Method
We will focus on simulating the V1 area of the Ventral Stream by using the HMAX
11

model. The features of the V1 area as follows:• The lateral geniculate nucleus(LGN which is the primary relay center forvisual information) and V1 are both divided into six layers.
• The pyramidal cell and stellate cell project inside the V1.
• The simple cell and complex cell process the information of the lines.
•

The complex cell will be abstract (the combination of the straight lines withsame angle from different positions).

• For the cells which react to the straight lines of different angles, here we
arrange them in columns, as it happens in V1.
•

At least, there are three paths for vision: shape recognition, motion
analysis and color processing. [2][3]To help researchers implement pattern recognition of astronomical data more
efficiently, we have made improvements to HMAX model in following aspects:
1. We used an improved new method to select patches.
2. We used more orientations (12) for the Gabor filter in order to acquire responses
from more different directions. The same parameters are applied to different scales inorder to reduce the number of variables and avoid instabilities.
3. We used SCI-KIT as our classification tools, to maximize speed and better
integration with python.

12

2.Neural Networks and Artificial Neural Networks
2.1 Intelligence and machine learning
The human cerebral cortex is a neurons square of approximately 1000 cm2and

2mm thickness. For visualizing, please imagine a cloth napkins, which is probably thesimilar size and thickness of the neocortex. The neocortex is divided into many
functional areas: the visual area, auditory area, language area and so on. Assume weobserve them under a microscope, and the physical characteristics of these different
regions are almost the same. There are some organizational structures in eachregion throughout the whole cerebral cortex.

Fig 2-1 The six layers in neocortex
The first organizational structure is called the layer. Overall, the neocortex has six
layers, wherein 5 layers include cells while 1 layer is composed almost by only
connections and no neurons[3]. The second functional organizational structure is13called the column. When the scientists explored what made the neurons become
active by using the probe, they found that the neurons crossed the different levels, by
responding to the same input by the mode of the vertical distribution.

According to the current study of the brain in biology, we believe that the essence of
the intelligence is the memory and the prediction. And the intelligence is not ansudden extraordinary ability coming from above, but an action force running throughthe entire evolutionary history of organisms.

There are three stages so far throughout the evolution of intelligence. The first stage
is from the single cell to an organism appearing the primary neural network. The socalled intelligence does not exist in someone biological body, but performs throughthe DNA, RNA or other forms of genetic evolution. However, in this process, it does
exhibit the memory (the DNA heredity), which is one of the natural property of theintelligence.

The second stage started from the appearing of the primary neural network to thestage of higher neural network. Through biological evolution, finally, there had a set oflower level of neural networks, such as the mollusks, the plants and so on. After
having such set of neural networks, they had been greatly improved in the adaptation
to the environment against the enemy and other related aspects.

The third stage started from the advanced neural network to nowadays. In the second

14stage, the biological neural network is not subject to change with different
environments at any time, but only by the DNA genetic generation which has too low
efficiency. So up to this stage, it began to appear the advanced neural network which
can adjust in real-time in different environments. Needless to say, humans are very
good at this stage. However, the footsteps of the intelligence evolution will not stop.
There will be even more efficient and advanced intelligence in the future.

Input(visual, hearing)

Recognition

Learning(Hierarchic(Classification,

processing )Store)

Output(prediction)

Fig 2-2 The flow chat of perception process
We believe that the Artificial Neural Networks established based on the abstraction
and simulation of some basic properties of human brain and Natural Neural Network
should have the following characteristics:

1. Self-learning function: for instance, in order to realize image recognition, we canjust input the different image templates and corresponding recognition results into theartificial neural network, so that the network can learn to recognize similar images
through the self-learning function.
2. Prediction storage function: this kind of prediction can be achieved by the feedback
artificial neural network.
3. Capable of finding out the optimal solution quickly: finding out the optimal solution
to a complex problem often needs a large amount of computation. However, using
the feedback Artificial Neural Networks specially designed for a problem can give full15

play to the high-speed computing ability of the computer and find out the optimal
solution quickly.

2.2 The Visual Cortex Structure and Pattern RecognitionFig 2-3 Our Brain can separate pictures of objects belonging to many categories
After the light enters the eye, it will be absorbed by the photo receptor on the retina,
which drives a series of chemical reactions into nerve signals, and then they begin to
be forwarded. The retina can actually be divided into more detailed layers, which canbe used as the integration processing on the visual signals of the first stage. After thephoto-stimulation of the retina converted into nerve signals, they will be transmitted
along the optic nerves, then the part of the optic nerves of the left and right eyes havethe first cross, then they go into an area of the brain below which is the one piece ofthe thalamus, the English abbreviation for it is the LGN (lateral geniculate nucleus).
The LGN is equivalent to a relay station for visual signals from the retina to the visualcortex V1. It mainly has a function of attention, as well as some regulating functions
for the feedback signals from some other regions of brain. In addition, until the visual16

signals arrive the LGN, all the images (to which each nerve cell responses) havecircle shapes, which have luminance contrasts between the middle and outer of thering (light inside and dark outside, or the dark outside and light inside), only by which
it will cause the responses from the retina or the LGN cell.

The LGN can also be initially distinguished by two paths: the M Pathway and the P
Pathway as the textbooks usually calls[4]. These signals of two paths are respectively
related to the "move" and the "shape color". These two signals will also be finallytransmitted to be processed in different areas. Then, as the previous picture of thehuman brain, the visual signals start from the retina, then go through the LGN (asrelay station), and then arrive the hind brain spoon, which is also the first station of
the visual cortex called the V1. The biggest difference between V1 and LGN is thatyou need a straight line to make a cell response, rather than an inner and outer circle
with a comparison of brightness. The LGN cells in the ventral Stream(There are two
streams in the visual cortex: the “dorsal stream” and the “ventral stream”) will only
react to the shape of circles, and the V1 cells will only react to the straight lines. The
V1 can actually been divided as: the simple cell and the complex cell.

17

Fig2-4 Simple cells in V1 area of visual cortex
In addition, in the V1 area of visual cortex, there is an model with six layers as shownin above map which describes the columns in V1 cell, and each column is on behalfof a group of cells which react to straight lines in someone direction. The cells thathave similar directions and angles in the brain are also close. However, the later
studies found that the pinwheel model should be more suitable.

After the study by Hubel and Wiesel, etc., the visual cortex cell in V1 is found to befurther divided into simple cells and complex cells. Just like mentioned, the simple
cell reacts to the straight lines at an angle (straight line, horizontal line, and theoblique line with an angle). The complex cells can capture spatially displaced
occurrences of similarly oriented features. So inside the visual cortex V1, it is thesimple cell and the complex cell to make the appropriate processing on the visualsignals, so that we finally recognize the square, the rectangular or the more complex
visual information. Up to now, it is not entirely clear what are the detailed
mechanisms after completing the process in the visual cortex V1. Semir Zeki [5] is18

the first author to propose that the visual signals are processed in parallel. The
current hypothesis propose that there are at least three or more visual paths which
process the images of motion, shape or form of the object, and the color. The paths
from the M channel and P channel of LGN to the six layers inside V1 for different
projections, and then the path from the V1 to the other cortical areas for processing,all of which include above three main visual paths.

Tomaso Poggio (the professor at MIT) proposed one most preliminary visual model.
In the neuro-science, he hoped to transform those visual research findings to the
Algorithm used in the information science, which can make the computer be closer tothe biological model to help people complete some work of the image classification.
We can get characteristics with high invariance and selectiveness through thesimulation and modeling of visual cortex tissue structure. These characteristics canhelp us to learn from a small number of training samples, so as to complete thepattern recognition in complex scenes.

19

3.Standard HMAX model
3.1 General methods (HOG, SIFT) for feature descriptors in computer vision
For feature extraction, Histogram of Oriented Gradient(HOG) method andScale-Invariant Feature(SIFT) method was the common method in computer vision.

Fig 3-1 The feature vectors from HOG method
The following is the HOG method for feature extraction from images :A. Image gradation (Images was seen as 3-dimensional(R,G,B) data).
B. Dividing the data in into several cells.
C. Calculating the gradient(orientation) of each pixel in every cell.
D. For each cell, the statistics of the gradient histogram is the descriptor of features.
[6]

Fig 3-2 The feature vectors from the SIFT method

20

The following is the Scale-Invariant Feature (SIFT) method for feature extraction from
A. Using scale-space filter for edge detection.
B. Finding the all positions of all of the key points.
C. Calculating the gradient magnitude and direction in each region.
Comparison with the HMAX model, the HOG features has no Scale-invariant andRotation-invariant. Change of the direction or size will change the detection rate
significantly. SIFT method has Scale-invariant and Rotation-invariant, but to getfeatures with this method is over dependence on the selection of the principal
directions.

3.2 Standard HMAX model
As mentioned above, the brain and the intelligence, as the most mysterious part ofhuman body, has been drawing the attention of neurologists for hundreds of years.
The enormous number of neurons and experimental data makes it quite difficult forpeople to understand the operating principle of the brain. Biologists have put forward
various theories to explain the cells behavior of V1 area. However, few studies havebeen made on a higher level of neuronal behavior, such as Contour Linking andFeature Grouping. In recent years, there has been a lot of research hypotheses
proposed by researchers, but these hypotheses just limited to the description of cells
behavior. Few researches have been made on how to realize object recognition—theultimate objective.

In the calculation model developed by Poggio based on the imitation of human brain

21

(HMAX), a multilayer model which is biologically similar to human brain is given to
realize object recognition. This model proved a great success and made a stir in the
research field of machine vision.

Fig 3-3 Standard HMAX model
The above model is what proposed by the professor Poggio from MIT. The standard
model contains 4 layers, “simple unit” S1 Layer, “complex unit” C1 Layer, “simple
unit” S2 Layer, “complex unit” C2 Layer[7].

HMAX uses scale-space and a spatial hierarchy to perform processing on small
sub-components of the image in multiple image scales/sizes. The hierarchy consistsof multiple Levels, with lower levels feeding data into the higher levels, one level tothe next. The bottom-most level takes in the original input image while the top-mostlevel performs the final classification decision. Each level will consist of one or more
22

Layers. Each layer is intended to handle differently scaled sizes of the original input
image in order to perform a sort of parallel processing of multiple object sizes. As we
ascend the hierarchy we slowly combine the scale layers by selecting the best
responses between 2 neighboring scales resulting in an overall best response across
many sizes. This gives an increased invariance to size when performingclassification.

In S1 Layer, an input gray-scale image is densely filtered by a battery of Gabor filters
at each scale and orientation. And at each pixel, all of the filters are centered.
The Gabor filters are described by:
2

G( x , y)=exp(−

Where

X =cos θ− ysin θ and2( X +γ Y )2π)cos ( )
2
λ
2σ

(1)

Y =sin θ+ ycos θ . λ represents the wavelength of

the sinusoidal factor, θ is the orientation of the Gabor function,

γ is the spatialaspect ratio.

The parameters of filters with 4 orientation and 16 scales as in the table below:
S1 Layer
Scale

Filter

Gabor

Gabor

Band S

Size sparameterparameter

σ

λ

2.8

3.5

3.6

4.6

Band 1

7×7
9×9

23

S1 Layer
Band 2

Band 3

Band 4

Band 5

Band 6

Band 7

Band 8

11×11
13×13
15×15
17×17
19×19
21×21
23×23
25×25
27×27
29×29
31×31
33×33
35×35
37×37

4.5

5.6

5.4

6.8

6.3

7.9

7.3

9.1

8.2

10.3

9.2

11.5

10.2

12.7

11.3

14.1

12.3

15.4

13.4

16.8

14.6

18.2

15.8

19.7

17.0

21.2

18.2
22.8
Table 3-1 The Gabor filter parameters in S1 Layer[7]The results from S2 Layer with the parameters conform to the biological experimental
datasets. But the defining too many parameters destroys the simplicity of the model.
In C1 layer, for each scale the model gets the local maximum value between 2neighboring scales . The S2 layer selects

N

patches of data randomly. And then,each patch is filtered by a RBF filter. The values of C2 Layer comes from thetraversing all of 8 groups of the scales, to find the maximum of the responses.

24Fig 3-4 The S2 layer select parts of the input as the synaptic weights
However, some problems are what this model needs to modify. Firstly, regarding theinformation on moving objects, the Aperture Problem involves that how to connect the
information in different areas to each other to be overall, which is called the Binding
Problem. For example, the neuron A reacts to the images moving to the left, while theneuron B reacts to the image moving to the right. But if a big image moves up as awhole, it is moving to the left or to the right from the regional point of view, for which
there is still no appropriate model. However, some research results, e.g., theexperimental results of IT (Inferior temporal cortex)from Keiji Tanaka found that the IT
neuron will only respond to some particular shapes, rather than simple geometricshapes such as the round or the square, etc.

Fig 3-5 The experimental results of IT neuron from Keiji Tanaka
25

Take above picture as an example, it shows a certain neuron reaction in IT [3]. The
reaction at the upper left corner of the picture is the biggest, which is a big circle witha small protrusion. The reaction is 1.0. However, if we turn 180 degrees, then theremaining reaction becomes to be 0.01. If the small protrusion is changed into thesquare, the remaining reaction is also 0.01. If there is no small protrusion, but onlywith a large circle in the middle, the reaction is actually 0.
Therefore, the processing mode of IT for handling object shape is still uncertain,
because it is not so obvious like V1. A group of neurons are reactive for straight lines
in a certain direction. Each neuron of IT only reacts to a certain kind of special shape,
while some features are important but some features can be taken away and will notaffect. Also because there still remains parts which needs continuing efforts from theresearcher. Therefore, we focus to simulate the S and C cells in V1 area which havebeen biologically and clearly researched and passed in HMAX model.

3.3 Support Vector Machine
Support Vector Machine (SVM) is a very popular and well known machine learningtechnique proposed by Vapnik et. which has been successfully applied to many
real-world classification problems from various domains. SVM is a supervised
learning model with associated learning algorithms that analyze data and recognize
patterns, and is used for classification. Due to its theoretical and practical advantages(such as solid mathematical background, high generalization capability and ability tofind global and non-linear classification solutions), SVM has been very popularamong the machine learning and data mining area.
26

The process of SVM is divided into two parts as depicted below. The is called thelearning part. Given a sample dataset, an SVM training algorithm builds a model inorder to classify the given data. The second part is called the prediction part.
Given new test data, it is classified into a category using the model which was built in
learning part.

Fig 3-6 The process of Support Vector Machine
The theory of Support Vector Machine initially comes from the disposal for the dataclassification. For the binary classification of the data, if we apply the neural network,
the system randomly generates a hyperplane and then moves it, then we know thatthe points (in the training set) belonging to different categories are just located at
different sides of the hyperplanes. As for the Linear Support Vector Machine, we
consider the training samples as { X i ,the input pattern, theYi

Y i }, where theXiis the example ofis the corresponding target output. At beginning, we

assume that the models represented by the subset

Y i =−1,+1

are linearly

separable, for separating the hyperplane format to decide the SVM classifier as:27

f (x )=sign(W T + b)

(2)

and the corresponding hyperplane isT(3)

W + b=0

Where W is the parameter vector, and b is the biased or offset scalar. Label y of thetest data is decided byf ( x ) . When W T + b>0 , then

y=−1

28

y=+1

otherwise4. System for astronomical data analysis
We develop a new improved version of HMAX model. We developed the system inpython because of its versatility and available libraries. The differences with thestandard model are as mentioned above, an improved method to select patches, use
more orientations (12) for the Gabor filter and used SCI-KIT as our classification
tools, to maximize speed and better integration with python.
4. 1 SDSS databaseFig 4-1 The images from Solar Digital Sky Survey（SDSS）Data Release 8
We used the Solar Digital Sky Survey（SDSS）Data Release 8 as our dataset. In
this release, all of the imaging data was taken by the SDSS imaging camera whichcontains totally over 14000 square degree of sky. In this study , we prepared thedataset with about 500 stars and 500 galaxies for sample training. And we used
about 5 groups each group contains 60 images for test our model.

294.2 Astronomical data Features
4.2.1 Initial processing
In the initial processing, the program is designed to take an input image and produce
12 resized scales of the image. The intent is that a range of image sizes will help thealgorithm become exposed to a greater range of object sizes. Since we keep theGabor filter size fixed (at say 11x11), as we decrease the image resolution the Gaborfilter is effectively covering a larger and larger area of the image. In this way we candetect orientated lines at a range of sizes from rather small to rather large. A single
layer then contains a set of data usually to represent a given size scaling. By having
multiple layers we can store multiple image scales at once and process then inparallel. As we ascend the hierarchy we slowly combine the scale layers by selecting
the best responses between 2 neighboring scales resulting in an overall best
response across many sizes. Layers have a concept of both discrete-space and what
we call retinal-space. The discrete space is simply the actual indices's corresponding
to a numpy 2d matrix that is storing the actual layer values (The pixel values for theimage). While retinal-space is a real-valued global space, where (0,0) indicate thecenter of the image and this location is consistent across all hierarchical levels
despite that each level will have discrete matrices of differing dimensions. Using theretinal-space we have a way of ensuring accurate comparisons of locations acrossdifferent layers in different levels.
We prepared five groups of layers to store the feature vectors in different process,and size of the layers is used as the table following[8]:30Input Layer

S1 Layer

C1 Layer

S2 Layer

C2 Layer

256×256246×246

47×47

44×44

1×1

214×214204×204

39×39

36×36

180×180

170×170

33×33

30×30

152×152

142×142

27×27

24×24

128×128

118×118

21×21

18×18

106×106

96×96

17×17

14×14

90×90

80×80

15×15

12×12

76×76

66×66

11×11

8×8

64×64

54×54

9×9

6×6

52×52

42×42

7×7

4×4

44×44

34×34

5×5

2×2

38×38

28×28

1×1

Table 4-1 The size of layers
4.2.2 Process in S1 Layer

Fig 4-2 Process in our HMAX model
The S1 Layer is applied on 12 resized versions of this image at different scales(256x256, 214x214, 180x180 down to 38x38 pixels). In higher mammals brain,
images are filtered along orientations. A Gabor filter is used to reproduce thisfunctionality. In our HMAX model each scale the Gabor filter is applied at 12 different
31orientations, for a total of 12x12 set of output data (12 scales by 12 orientations).
The Gabor filters are described by[8]:22( X +γ Y )2π
G( x , y)=exp(−
)cos(
)
2
λ
2σ

Where

X =cos θ− ysin θ and(4)

Y =sin θ+ ycos θ . We adjusted the same filter

parameters for all of the scales,orientation,and wavelength . And in our model, we
make

γ =0.3,λ =5.641,

σ =4.5128. The result value will be returned and is

expected to then be stored in the S1 network layer.
4.2.3 Process in C1 Layer
After the S1 layer is processed, the 12x12 group of data are received by the C1 layer.
In C1, a major rescaling is performed as happens in the hierarchical information flowin the mammal brain. In our algorithm each resized group is compared with theadjacent smallest sized group of data (for example 256x256 compared with
214x214). The comparison is made with normalized XY coordinates on a 3x3 grid.
The biggest data value is selected and a single data point is generated. All the image
is scanned and a new smaller group of data is generated (for example, 47x47). There
are 12 orientations for each group size, so 12 of these resized groups are generated
this way. The process continues with all the other sizes (214x214 compared with
180x180 and so on) until a new set of smaller data groups are created. The groups
are these sizes (47x47, 39x39, 33x33 down to 5x5). Finally, because of thiscomparison in couples, one of the sizes is lost and after C1 operations, we have 11
sizes , and 12 orientation, for a total of 11x12=132 groups. This elaboration is done toremove noise in scale-independent pattern recognition processes observed in
mammal.
32

4.2.4 Process in S2 Layer
In S2 layer, we selected 4075 patches of data. we only store it as a learned template
patch if:
A. At least 35% of the cells are non-zero.
B. The patch has less than 35% similarity to any existing learned patch.

If we have 10 learned patches already, and our similarly threshold is 90%, then we
will not accept a new patch unless its RBF value is below 90%similar to all theexisting patches. This helps ensure a minimal level of uniqueness/variety among
learned patch templates. Once we have enough template patches learned we canrun the filter in inference. For inference each patch in the current C1 Composite iscompared against all learned template patches and scored using a Gaussian
Radial-Basis-Function similarity value. All of these values are sent as output to thenext layer C2.

The size of these patches is 4x4x12 or 8x8x12 or 16x16x12 chosen at random andplaced at random position. The 12 appears because we have 12 orientations.
Because of normalization of coordinates, these patches have the same relative area
on each scale. The Gaussian Radial-Basis-Function is applied:R( X i , P j )=exp (−In our case, we set

σ=1.6 and( X i −P j )2)
2 σ2 α(5)

α=1 ( α is the normalizing factor for patch

sizes can vary), The output of this layer is a new group of data of size

A× A×4075 ,where A is the original size of the group. The 4075 points are the maximum value of33

the Gaussian filtering above. After this stage we have 11 group of data, each of them
A× A×4075 in size, where A is the original group size (47x47, 39x39, 33x33 down
to 5x5 as above).

4.2.5 Process in C2 Layer
These 11 groups are fed to the C2 layer that in the same fashion as C1 layer, it
compare different patches on a 3x3 grid. This time the comparison is not made on
two adjacent sizes, but is done on 6 of them. For example the group A=47 iscompared with A=39, A=33 and so on. A single maximum value is determined. This
process is repeated another group of 6 different sizes. At the end two group of data ofsize A=39 and A=21 is generated. Each group of data has size

A× A×4075 .

Now spatial dependence is removed. For each AxA sheet data in each group, themaximum value is taken. So each

A× A×4075 group is reduced to a

one-dimensional vector of size 4075. We do that on the two groups and join the
results. So the output of C1 is a single one-dimensional vector of size 8150. This final
vector represent the generalization of the original image features, and we call it fromnow on “the features” embedded in the image.

4.3 Data Training And Prediction
We built the learning and prediction of vectors process by Using python. We usednumpy module to store vectors and scikit-learn module for machine learning process.
In our model training and prediction was used as following:
A. Normalization in the input space. The same axle of all the sample features should34

be normalized [9].
B. We set the type of Support Vector machine to “C-SVC”, the type of kernel function
to radial basis function[10].

Fig 4-2 Sample A(Star), Histogram of Sample A, C2 features of Sample A

35

Fig 4-3 Sample B(Galaxy), Histogram of Sample B, C2 features of Sample B

36

5. Dataset Prediction
5.1 Test result of Prediction
The test dataset consists of 5 groups, each group has 200 images from SDSS. The
accuracy rate for our model is in the table following:
TestingdatasetLearning process timefor eachsample(average)

Prediction process
time for eachsample(average)

AccuracyGroup1

1 Hour 20 Minutes

0.17 seconds

86%Group20.17 seconds

91%

Group30.16 seconds

72.5%

Group40.17 seconds

89.5%

Group50.19 seconds

84%

In this research, we have used Hmax Model to carry Astronomical data andclassification. The Accuracy have been improved to 80% and in the process, we havesimulated the information delivering method of biological brain and achieved effective
features. We also found the problem of long timescales, Now we are trying to
improve this problem by using admixture programming with C Language and python.

5.2 Conclusion and Prospect
By improving HMAX model, this study has established an astronomical image
recognition platform based on Python. We improved the selection method of patches
and increased the number of Gabor filter orientation. We selected positive andnegative samples from SDSS database randomly, tested our model and achieved
fruitful results. HMAX model is an object recognition model in brain resembling
structure which works very well. It verifies Gabor filter and multilayer feature37

combination based on biological features and extract the features with invariance andselectiveness by maximizing the space scale filters. The classification of feature
vectors of each image or each group of data can be completed by using machinelearning. However, Support Vector Machine has removed its biological consistency tosome extent at the final stage.

5.3 Research in the future
In the present study, this system is based on the characteristics of static image or thedata identification. We use the Support Vector Machine which doesn't consider time
sequence. But in reality, most of the image and data are often accompanied by thetime. If we assume we do not use the time sequence, we almost can not infer
anything from the dynamic images or audio information. Human eyes can understandthe static scene. Therefore, the visual processing doesn’t always require inputting of
the temporal information. However, in the usual visual scene, we are constantly
moving our eyes, head and body, and there are too many moving objects around us.
After many generations human can recognize everything in this world, quickly. For
the general conditions of vision, audition and thigmesthesia (touch sense) [11], thereasoning always requires the input of temporal change. So in the next step of study,
we hope to join the dynamic timing of the data into the existing algorithms. Simply
speaking, it will be a set of data flow of the spatial characteristic data varying with thetime. This new algorithm still needs a lot of data for training. Just like that if you wantto identify different varieties of ships by observing many different types of vessels
instead of just looking at someone type. In future, we want to realize an algorithm will38

learn the time series of the input stream, for example, you can build a model of one
pattern following another. The difficulty lies in this algorithm is that you don’t know
when to start the sequence and when to end, and the possible overlap at the sametime point.

39

Reference
1. Y. D. Tian, L. Q. Zhang.Relevant, Algorithms for General Object Recognition Basedon Feature Combination.Shanghai Jiao Tong University Journal,2007.
2. J. Hawkins, S. Blakeslee, On Intelligence, 2004.
3. H. Kolb, R. Nelson, E. Fernandez, B. Jones, The Organization of the Retina andVisual System,1995.
4. Fang F, He S., Cortical responses to invisible objects in the human dorsal andventral pathways. Nature neuroscience, 2005.
5. Zeki S., A Vision of the Brain[M]. Blackwell scientific publications, 1993.
6. Kishino T, Zhe S, Micheletto R. A fast and precise HOG-Adaboost based based
visual support system capable to recognize Pedestrian and estimate theirdistance,2013
7.T. Serre, L. Wolf, and T. Poggio. Object recognition with features inspired by visualcortex. In CVPR, San Diego, June 2005.
8.J. Mutch and D. G. Lowe.Object class recognition and localization using sparsefeatures with limited receptive fields. International Journal of Computer Vision. Vol. 56

9. Graf A B A, Borer S. Normalization in support vector machines, Pattern
Recognition, 2001.
10.C. Elting, C. A. L. Bailer-Jones, K. W. Smith. Photometric Classiﬁcation of Stars,
Galaxies and Quasars in the Sloan Digital Sky Survey DR6 Using Support Vector
Machines. American Institute of Physics Conference Series. Vol. 1082. pp. 9-14 ,
40

2008
11. Numenta org, Hierarchical temporal memory including HTM Cortical Learning
Algorithms ,2011.

41

研究概要（日本語）
視覚野ニューロンネットワーク情報処理に基づいた、天文データの識別システムの研究
概要: 人間の視覚野は、目から得たデータを処理し、学習と記憶に基づき、パターン認識をしている。脳の働き方を模倣した、学習機能を持つアルゴリズムは、一般的なデータパターン（実験データ等）の認識率が高い。従来の研究では、輝度の勾
配方向と勾配強度を利用しているが、認識の精度は低い。これは、宇宙電波望遠鏡の観測データはノイズが多く複雑かつ、情報量が膨大なためである。これを改善するために、人間の視覚野神経回路網情報処理モデルと機械学習技術を利用して、観測データを精度良くパターン認識できるシステムを開発する。本研究では、MIT の
Poggio 教授によって発明された HMAX モデルを用いた。HMAX モデルは、生理学的実験に基づいた大脳視覚野の情報処理方法を利用し、データの特徴抽出を行うモデルである。具体的には、 V １野と V ２野の特徴抽出細胞および V4 野を経て IT 野に至る階層的な処理である。このモデルを利用し、視覚野単純型細胞と複雑型細胞の情報処理プロセスを次のような４層で行う。まず、S1 層で処理データと各方向のガボールフィルタを畳み込み計算を行って、次に S2 層で S1 層の直近データを統合する。そして、S2 層でデータパッチを出力し、C2 層で全部のデータを統合する。
最後に、C2 層処理後のデータは、サポートベクターマシン（SVM）でパターンの学習と予測を行う。

42

謝辞：
終始熱心なご指導を頂いた生命のシステム研究科の先生達に感謝の意を表します。
日本にいる修士の２年間にあたり、終始適切な助言を賜り、また丁寧に指導して下さった木下郁雄教授、大月俊也教授、Robert Knaly 準教授 に感謝します。最後まで一緒に頑張って来たミケレット研究室の同期の皆様，研究を手伝ってくれた後輩に心より感謝しております。学業と生活に Ruggero Micheletto 教授から、いろいろな応援をいただき、本当に有難うございました。それで、NITORI 国際奨学財団から十分な経済面のご支援のおかげで、自分の学業に専念することができました。
今後、博士課程をより一層勤勉に勉強し、いろいろな交流活動にも力を傾けようと思う。さらに、世界一流の研究能力を把握することを目指し、努力していきたいと思う。
来日してから、いろいろなことを経験し、初めてじ自分の成長を実感した。皆のおかげで、勉強も生活両立することができて、研究を順調に終わりました。帰国後、社会に貢献しようと思う。また、皆さんとの縁を銘記し、恩返したいと感じています。

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Research Products
Conference Presentation:
Sun Zhe, “A fast and precise HOG-Adaboost based based visual support systemcapable to recognize Pedestrian and estimate their distance.”, the 17th International
Conference on Image Analysis and Processing, 2013.
Sun Zhe, “Study of Information Processing Systems Based on Neural Network of
Visual Cortex”， Sensory Substitution Symposium， 2013

Papers in International Journals:
T. Kishino, Sun Zhe, R. Micheletto, “A Fast and Precise HOG-Adaboost Based Visual
Support System Capable to Recognize Pedestrian and Estimate Their Distance”,
Lecture Notes in Computer Science, Vol. 8158, pp 20-29 (2013)

Submitted to International Journals:
Sun Zhe, Ruggero Micheletto, “Emergence of intelligent behavior from a minimalistic
stochastic model for the navigation of autonomous Robots”
T. Kishino, Sun Zhe, R. Micheletto, “Cross-modal codification of images with auditory
stimuli: an efficient language for the visually impaired ”

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