APPENDIX A THE NEURO BIOLOGY OF VISION AND ATTENTION A.1

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APPENDIX A

THE NEURO BIOLOGY OF VISION AND ATTENTION

A.1 Introduction

To understand the concept to human attention, it is worth to regard the

processing involved in human visual system as discussed in the following sections.

A.2 The Human Visual System

The light that achieves the eye is projected onto the retina and from there the

optic nerve transmits the visual information to the optic chiasm. From there, two

pathways go to each brain hemisphere; the collicular pathway leading to the

Superior Coliculus (SC) and, more important, the retino-geniculate pathway, which

transmits about 90% of the visual information and leads to the Lateral Geniculate

Nucleus (LGN). From the LGN, the information is transferred to the primary visual

cortex (V1). Up to here, the processing stream is also called primary visual pathway.

From V1, the information is transmitted to the ―higher’ brain areas V2-V4, infero

temporal cortex (IT), the middle temporal area (MT or V5) and the posterior parietal

cortex (PP).

A.3 The Eye

The light that enters the eye through the pupil passes through the lens, travels

through the clear vitreous humor that fills the central chamber of the eye and finally

reaches the retina at the back of the eye. The retina is a light-sensitive surface and is

densely covered with over 100 million photosensitive cells. The task of the

photoreceptors is to change the electromagnetic energy of photons into neural

activity that is needed as input by neurons.

There are two categories of photoreceptor cells in the retina: rods and cones.

The rods are more numerous, about 120 million, and are more sensitive to light than

the cones. However, they are not sensitive to color. The cones (about 8 million)

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provide the eye’s sensitivity: among the cones, there are three different types of

color reception: long wavelength cones (L-cones which are sensitive primarily to

the red portion of the visible spectrum (64%), middle-wavelength cones (m-cones)

sensitive to the green portion (32%), and short wavelength cones (s-cones) sensitive

to the blue portion (2%). The cones are much more concentrated in the central

yellow spot known as the macula. In the center of that region is the fovea centralis

or fovea, a 0.3mm diameter rod free area with very thin densely packed cones. It is

the center of the eye’s sharpest vision. This arrangement of cells has the effect that

visual scene is not perceived with the same resolution in all parts.

Figure A.1 Primary Visual pathway in the brain

Rather perceived in a small area with high resolution and the whole surrounding

only diffuse and coarse.

The photoreceptors are connected via bipolar cells with the ganglion cells.

Whereas photoreceptors and bipolar cells respond by producing graded potentials,

the ganglion cells are the first cells which produce spike discharges and so transform

the analog signal into a discrete one. The receptive field of a ganglion cell is circular

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and separated into two areas: a center area and a surround area. There are two

different types of cells: a on-center cells which respond excitatory to light at the

center and off-center cells which respond inhibitory to light at the center. The

area surrounding the central region always has the opposite characteristic. There are

small ganglion cells and large ones. P ganglion cells receive their input just from the

cones are are more sensitive to color than to black and white, whereas the M

ganglion cells receive input from both rods and cones are more sensitive to

luminance contrasts.

The main contribution is the color opponency from the outputs of the three

cone system. The red-green contrast is derived from combining the excitatory

input from the L-cones and the inhibitory input from the M-cones, essentially

subtracting the signals from the L and M cones to compute the red-green

component of the stimulus (L-M). The green-red contrast is equally determined by

(M-L). The blue-yellow contrast is derived from the excitatory output of S-cones

and the inhibitory sum of the M and L cones (S-(L+M)) and the yellow-blue

contrast is determined by the excitatory sum of the M and L cones and the

inhibitory output of the S-cones ((M+L)-S). Finally, the luminance contrast is

derived by summing the excitation from all three cone types (S+M +L) (on-off

contrast) or by summing their inhibitory output (-S-M-L) (off-on contrast).

Fig A.2: The human eye

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(a) (b)

Fig A.3 The perception of the retina shown in (b) for original image (a) [31]

A.4 The Optic Chiasm

The axons of the ganglion cells leave the eye via the optic nerve, which leads

to the optic chiasm. Here, the information from the two eyes is divided and

transferred to the two hemispheres of the brain: one half of each eye’s information is

crossed over to the opposite side of the brain while the other remains on the same

side. The effect is, that the left half of the visual field goes to the right half of the

brain and vice versa. From the optic chiasm, two pathways go to each hemisphere:

the smaller one goes to the superior colliculus, which is involved in the control of

eye movements. The more important pathway goes to the LGN of the thalamus and

from there to higher brain areas.

Figure A.4 The double opponency cells

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A.5 The Lateral Geniculate Nucleus (LGN)

The Lateral Geniculate Nucleus (LGN) consists of six main layers composed

of cells that have centre-surround receptive fields similar to those of retinal ganglion

cells but larger and with stronger surround. Four of the LGN layers consist of

relatively small cells, the parvocellular cells, the other two of larger cells, the

magnocellular cells. The parvocellular cells process mainly the information from the

P – cells of the retina and are highly sensitive to color, especially to red-green

contrasts, whereas the magnocellular cells transmit information from the M-cells of

the retina and are highly sensitive to luminance contrasts. Below those six layers lie

the koniocellular sub layers, which respond mainly to blue-yellow contrasts. From

the LGN, the visual information is transmitted to the primary visual cortex at the

very back of the brain.

A.6 The Primary Visual Cortex (V1)

The primary visual cortex is with some 200 million cells the largest cortical

area in primates and is also one of the best investigated areas of the brain. It is

known by many different names. Besides the primary visual cortex, the most

common ones are V1 and the striate cortex.

V1 is essentially a direct map of the field of vision, organized spatially in the

same fashion as the retina itself. Any two adjacent areas of the primary visual cortex

contain information about two adjacent areas of the retinal ganglion cells. However,

V1 is not exactly a point-to-point map of the visual field. Although spatial

relationships are preserved, the densest part of the retina, the fovea, takes up a much

smaller percentage (1%) of the visual field than its representation in the primary

visual cortex (25%).

The primary visual cortex contains six major layers, giving it a striped

appearance. The cells in V1 can be classified into three types: simple cells, complex

cells, and hypercomplex cells. As the ganglion cells, the simple cells have an

excitatory and an inhibitory region. Most of the simple cells have an elongated

structure and, therefore, are orientation selective, which means, they fire most

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rapidly when exposed to a line or edge of a particular direction [75]. Complex cells

take input from many simple cells. They have larger receptive field. Furthermore,

they are highly nonlinear and sensitive to moving lines or edges. Hypercomplex

cells, in turn, receive as input the signals from complex cells. These neurons are

capable of detecting lines of a certain length or lines that end in a particular area.

A.7 The Extrastriate Cortex And The Visual Pathways

From the primary visual cortex, a large collection of neurons sends

information to higher brain area. These areas are collectively called extrastriate

cortex, in opposite to the striped architecture of V1. The areas belonging to the

extrastriate cortex are V2, V3,V4, the infero-temporal cortex(IT), the middle

temporal area (MT or V5) and the posterior parietal cortex (PP). The notation V1 to

V5 comes from the former belief that the visual processing would be serial.

On the extrastriate areas, much less in known than on V1. It was later found

that the processing of visual information is highly parallel. Some of the areas

process mainly color, some form, and some motion. The functional separation

already started in the retina with the M-cells and P-cells and results in several

pathways leading to different brain areas in the extrastriate cortex. The statements

on the number of existing pathways differ: the most common belief is that there are

three main pathways, one color pathway, one form pathway, and one motion

pathway which is also responsible for depth processing. Other researchers mention

four pathways by separating the motion pathway into one motion and one depth

pathway whereas some mention some color, one motion and two form pathways.

The reason for this discordance of the extrastriate cortex has only started several

years ago and its functionality is not completely understood.

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Figure A.5 The visual processing pathway.

The color and form pathways result from the P-cells of the retina and the

parvocellular cells of the LGN, go through V1, V2, and V4 and end finally in IT, the

area where the recognition of objects takes place. In other words, IT is concerned

with the question of ―what‖ is in a scene. Therefore, the color and form pathway

together are also called the what pathway. Other names are the P pathway or ventral

stream because of its location on the ventral part of the body. The motion ( and

depth) pathway result from the M-cells of the retina and the magnocellular cells of

the LGN, go through v1, V2, V3, MT(V5), and the parieto occipale area(PO) and

end finally in PP, responsible for the processing of motion and depth. Since this area

is concerned with the question of ―where‖ something ins in a scene, this pathway is

also called where pathway. Other names are the M pathway or dorsal stream because

it is considered to lie dorsally. The distinction into ―where‖ and ―what‖ pathway

traces back to [122]; a visualization of these pathways is shown in Figure A.5

Some cells respond to color, few only to luminance, some have chromatic

preference for red, green, yellow or blue and also to oriented edges. Some response

to more than one feature and hence a certain area of brain does the processing. The

processing of the visual information is usually bi-directional.

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APPENDIX B

SCALE INVARIANT FEATURE TRANSFORM (SIFT)

B.1 Introduction

The Sift keys are highly distinctive, which allows a single feature to

be correctly matched with high probability against a large database of features using

a staged filtering approach. The first stage identifies key locations in scale space by

looking for locations that are maxima or minima of a difference-of-Gaussian

function. Each point is used to generate a feature vector that describes the local

image region sampled relative to its scale-space coordinate frame.

The key objects in the key frame are annotated using Scale Invariant Feature

Transform (SIFT) algorithm. The Input key frame is subjected to SIFT algorithm

[26] for the extraction of SIFT features. The extracted SIFT features are matched

against the SIFT database which consist of SIFT features for trained set of objects. If

there is a strong evidence for presence of object in the key frame, the key frame is

annotated along with the keyword for the object. Since, Video contains the same

objects at different scale, rotation and also it may subject to partial occlusion.

Therefore, to handle these special cases, a powerful algorithm SIFT is used here to

annotate the objects. The flow chart is shown in Figure B.1.

Figure B.1: Key Objects Annotator.

Key

frame SIFT

features

extraction

SIFT Feature

database

(training set)

Matching Annotation

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This approach is based on a model of the behaviour of complex cells in the

cerebral cortex of mammalian vision. The resulting feature vectors are called SIFT

keys. Following are the major stages of computation in generating the set of image

features:

Scale-space peak selection: The first stage of computation must search over

all scales and image locations, but it can be implemented efficiently by using a

difference of-Gaussian function to identify potential interest points that are invariant

to scale and orientation.

Orientation assignment: One or more orientations are assigned to each key

point location based on local image properties. All future operations are performed

relative to the assigned orientation, scale, and location for each feature, providing

invariance to these transformations.

Key point descriptor: The local image gradients are measured at the selected

scale in the region around each key point, and transformed into a representation that

allows for local shape distortion and change in illumination.

B.2 Scale-Space Peak Selection

The first stage of key point detection is to detect locations that are invariant

to scale change of the image can be accomplished by searching for stable features

across all possible scales, using a scale space kernel Gaussian function. Therefore,

The scale space of an image is defined as a function, L(x, y, σ), that is produced from

the convolution of a variable-scale Gaussian, G( x, y, σ), with an input image, I (x, y):

L( x,y, ) G( x, y, ) * I x,y (B.1)

Where * is the convolution operation in x and y , and

2 2 2

2

12

2( x , y , )G e x y / (B.2)

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To efficiently detect stable key point locations in scale space, using scale-

space peaks in the difference-of-Gaussian function convolved with the image,

D (x, y, σ), which can be computed from the difference of two nearby scales

separated by a constant multiplicative factor k:

D( x, y, ) (G( x, y, k ) G( x, y, ))* I x, y (B.3)

L( x, y, k ) L( x, y, ) (B.4)

Thus D can be computed by simple image subtraction of smoothed images (L).

Figure B.2: Difference of Gaussian Pyramid.

An efficient approach to construction of D (x, y, σ) is shown in Figure B.2.

The input image is incrementally convolved with Gaussians to produce images

separated by a constant factor k in scale space, shown stacked in the left column.

Adjacent image scales are subtracted to produce the difference-of-Gaussian images

shown on the right. Once a complete octave has been processed, the Gaussian image

is resampled that has twice the initial value of σ by taking every second pixel in each

row and column.

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B.3 Local Extrema Detection

In order to detect the local maxima and minima of D (x, y, σ), each sample

point is compared to its eight neighbors in the current image and nine neighbors in

the scale above and below (see Figure B.3). It is selected only if it is larger than all

of these neighbors or smaller than all of them. The cost of the check is reasonably

low due to the fact that most sample points will be eliminated following the first few

checks.

Figure B.3: Maxima and minima of the difference-of-Gaussian images.

B.4 Orientation Assignment

By assigning a consistent orientation to each key point based on local image

properties, the key point descriptor can be represented relative to this orientation and

therefore achieve invariance to image rotation. The scale of the key point is used to

select the Gaussian smoothed image, L, with the closest scale, as all computations

must be performed in a scale-invariant manner. For each image sample, L(x, y), the

gradient magnitude, m(x, y), and orientation, ө( x ,y), is precomputed using pixel

differences:

2 21 1 1 1m x, y ( L( x , y ) L( x , y )) ( L( x, y ) L( x, y )) (B.5)

1e x , y tan L x,y 1 L x, y 1 / L x 1, y L x 1, y (B.6)

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An Orientation histogram is formed from the gradient orientations of simple

points within a region around the keypoint the orientation histogram has 36 bins

covering the 360 degree range of orientations. Each sample added to the histogram

is weighted by its gradient magnitude and by a Gaussian-weighted circular window

with a σ that is 1.5 times that of the scale of the keypoint. Peaks in the orientation

histogram correspond to dominant directions of local gradients. The highest local

peak in the histogram is detected, and then any other local peak that is within 80%

of the highest peak is used to also create a key point with that orientation. Therefore,

for locations with multiple peaks of similar magnitude, there will be multiple key

points created at the same location and scale but different orientations.

B.5 Key Point Descriptor

The previous operations have assigned an image location, scale, and

orientation to each key point. These parameters impose a repeatable local 2D

coordinate system in which to describe the local image region, and therefore provide

invariance to these parameters. The next step is to compute a descriptor for the local

image region that is highly distinctive yetis as invariant as possible to remaining

parameters, such as change in illumination or 3D viewpoint.

Figure B.4 illustrates the computation of the key point descriptor. First the

image gradient magnitudes and orientations are sampled around a key point, using

the scale of the key point to select the level of Gaussian blur for the image. For

efficiency, the gradients are precomputed for all levels of the pyramid as described

in Section B.4. These are illustrated with small arrows at each sample location on

the left side of Figure B.4. A Gaussian weighting function with σ equal to one half

the width of the descriptor window is used to assign a weight to the magnitude of

each sample point. This is illustrated with a circular window on the left side of

Figure B.4, although, of course, the weight falls off smoothly. The purpose of this

Gaussian window is to avoid sudden changes in the descriptor with small changes in

the position of the window, and to give less emphasis to gradients that are far from

the center of the descriptor.

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Figure B.4: Key Point Descriptor.

The key point descriptor is shown on the right side of Figure B.4 shows eight

directions for each orientation histogram, with the length of each arrow

corresponding to the magnitude of that histogram entry. The descriptor is formed

from a vector containing the values of all the orientation histogram entries,

corresponding to the lengths of the arrows on the right side of Figure B.4. The figure

shows a 2x2 array of orientation histograms, whereas our experiments used 4x4

arrays of histograms with 8 orientation bins in each, forming 4x4x8 = 128 element

feature vector for each key point. These key point descriptors are highly distinctive,

which allows a single feature to find its correct match with good probability in a

large database of features.

B.6 Matching

The key point descriptor of 128 vector dimension is stored in the database

for the training set of objects. Once the test image is obtained i.e., the key frame

from video summarization module is processed for sift features and these features

are matched against the database using Euclidean distance. If there is a strong

evidence for the presence of object, the object tag is annotated with the key frame.

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APPENDIX C

TOOLBOXES

C.1 Saliency Toolbox

The Saliency Toolbox is a collection of Matlab functions and scripts for

computing the saliency map for an image, for determining the extent of a proto-

object, and for serially scanning the image with the focus of attention. It has been

cited in more than 100 papers.

System requirements:

Any computer and operating system that runs Matlab Release 13 or later

Image Processing Toolbox. The toolbox contains pre-compiled binary mex files for

MS Windows, Mac OS X (both Power PC and Intel Macs), and Linux (32 bit and 64

bit). The source code can be compiled on any system with the GNU C compiler gcc.

The Saliency Toolbox is in part a reimplementation of the iNVT toolkit at

Laurent Itti's lab at the USC. This toolbox complements the iNVT code in that it is

more compact (about 5,000 versus 360,000 lines of code) and easier to understand

and experiment with, but it only contains the core functionality for attending to

salient image regions.

Although time critical procedures are contained in mex files, processing an

image with the Saliency Toolbox in Matlab takes longer than with the iNVT code.

Whenever processing speed or feature richness is paramount, the iNVT code should

be preferred. For computing the saliency map or attending to salient proto-objects in

an image in a transparent and platform independent way, the Saliency Toolbox is a

good choice.

C.2 WEKA Introduction

WEKA stands for Waikato Environment for Knowledge Analysis. WEKA is

a collection of machine learning algorithms for data mining tasks. The algorithms

http://gcc.gnu.org/

165

can either be applied directly to a dataset or called from your own Java code. WEKA

contains tools for data pre-processing, classification, regression, clustering,

association rules, and visualization. It is also well-suited for developing new

machine learning schemes.

Figure C.1: WEKA GUI Window.

The GUI Chooser consists of four buttons—one for each of the four major

WEKA applications—and four menus. The buttons can be used to start the

following applications:

Explorer: An environment for exploring data with WEKA.

Experimenter: An environment for performing experiments and conducting

statistical tests between learning schemes.

Knowledge Flow: This environment supports essentially the same functions as

the Explorer but with a drag-and-drop interface. One advantage is that it

supports incremental learning.

Simple CLI: Provides a simple command-line interface that allows direct

execution of WEKA commands for operating systems that do not provide their

own command line interface.

A set of data items, the dataset, is a very basic concept of machine learning.

A dataset is roughly equivalent to a two-dimensional spreadsheet or database tab. A

dataset is a collection of examples. Each Instance consists of a number of attributes,

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any of which can be nominal (= one of a predefined list of values), numeric (= a real

or integer number) or a string (= an arbitrary long list of characters, enclosed in

―double quotes‖).

Figure C.2 Screenshot of WEKA classifier Window.

A classifier model is an arbitrary complex mapping from all-but-one dataset

attributes to the class attribute. The specific form and creation of this mapping, or

model, differs from classifier to classifier. There are various approaches to

determine the performance of classifiers. The performance can most simply be

measured by counting the proportion of correctly predicted examples in an unseen

test dataset. This value is the accuracy, which is also 1-ErrorRate. A more elaborate

method is cross-validation. Here, a number of folds n is specified. The dataset is

randomly reordered and then split into n folds of equal size. In each, iteration, one

fold is used for testing and the other n-1 folds are used for training the classifier. The

test results are collected and averaged over all folds. This gives the cross-validation

estimate of the accuracy. The folds can be purely random or slightly modified to

create the same class distributions in each fold as in the complete dataset. In the

latter case the cross-validation is called stratified.

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APPENDIX D

IMPLEMENTATION DETAILS

D.1 Sample Image Dataset

The sample images used in chapters 4, 6, 7 and 9 are shown in this section.

The signboard images are classified as BIKE, PEDESTRIAN and CROSSING

images as shown in Figure D.1, D.2, D.3 respectively. The car images used for

tracking application is shown in Figure D.4.

(a) Bike – 1 (b) Bike – 2

(c) Bike – 3 (d) Bike - 4

(e) Bike – 5 (f) Bike – 6

Figure D.1: Sample Bike Signboard Images [a-f] used in Chapter 6, 7 and 8.

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(a) Crossing – 1 (b) Crossing - 2

(c) Crossing – 3 (d) Crossing – 4

(e) Crossing – 5 (f) Crossing - 6

Figure D.2: Sample Crossing Signboard Images[a-f] used in Chapter 6,7 and 8.

169

(a) Pedestrian– 1 (b) Pedestrian– 2

(c) Pedestrian– 3 (d) Pedestrian– 3

(e) Pedestrian– 5 (f) Pedestrian– 6

Figure D.3: Sample Pedestrian Signboard Images [a-f] used in Chapter 6, 7

and 8.

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(a) Car - 1 (b) Car - 2

(c) Car - 3 (f) Car - 4

(e) Car - 5 (f) Car - 6

Figure D.4: Car Frames used for Tracking Application in Chapter 7.

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a) Ballons b) Way c) Medtan

d) Cones e) Sign f) Entry

g) Info Board h) Left i) Main

j) No-Entry k) No-Parking l) Computer

m) Vehicle n) Yellow o) Round

p) School - Ahead

Figure D.5: General Datasets used for Testing the Bottom-Up System in

chapter 4.

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D.2 Sample Values Obtained for the Bottom-Up Selective Tuning

Visual Attention Model

The various samples values for various images obtained by the selective tuning

model discussed in chapter 4 is shown in this Table D.1. In this table Order indicates

the hierarchy of attention obtained by the model , Value indicates the maximum

value computed by the selective tuning network in the salient region, Salient

indicates the Value with a constant factor added to it. The C_color and C_in

indicates the Color conspicuity and Intensity Conspicuity values for the salient

region. The RG and BY indicates the Red-Green and Blue_Yellow Feature values

for the salient region. The name of the images that are made bold in the table will

give the result as identified by the survey.

TABLE D.1 Sample Values obtained for bottom-up selective tuning visual

attention model

Name Order Value Salient C_color C_in RG BY

Ballons 1 435444 66243210 384868 47766 49434 335434

2 282739 57001250 315012 30992 32292 282720

3 64261 49401640 297856 37124 27712 270144

4 106619 43343328 382580 43420 34966 347614

5 372335 33003918 233704 29668 51938 181766

Boat 1 470863 45598756 452742 68496 152724 300018

2 441233 33329616 401464 46426 174502 226962

3 256428 22979014 405244 49892 129252 275992

4 188898 15822056 231420 25296 78544 152876

5 183864 12436012 54988 18864 11998 42990

Butterfly 1 483271 68919050 670994 68238 244718 426276

2 504328 57548394 504090 55528 160166 343924

3 294341 45121740 490924 47044 143620 347304

4 416979 38052280 485290 59988 126494 358796

5 289609 31167112 450960 46870 145878 305082

Cherry 1 629335 55389572 349194 38688 152488 196706

2 527067 46782752 314528 31776 134818 179710

3 39621 35202104 283026 30522 105100 177926

4 305105 24489434 230852 32252 85082 145770

Deer 1 527616 47817368 488970 53972 47128 441842

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2 322089 37318200 576812 57660 106818 469994

3 127859 28838092 450770 47968 39160 411610

4 167607 23093532 351008 34348 38188 312820

5 132990 18447524 411598 48390 33274 378324

Eye 1 146681 33037428 519776 41408 71716 448060

2 228975 26186044 381838 37478 42732 339106

3 275776 16319396 499272 50884 75040 424232

4 73254 11140050 106690 9226 16984 89706

5 29143 7460940 161132 11994 9462 151670

Horse 1 205190 31147678 447114 51346 151178 295936

2 117733 22391024 394178 47768 149630 244548

3 73942 14066198 300000 38280 113144 186856

4 75058 9021824 38122 41880 - -

Molecules 1 444649 79716622 605008 68552 153566 451442

2 461674 66841548 554804 65902 95248 459556

3 350511 54474556 599858 63222 43566 556292

4 490710 45995802 622530 60194 57054 565476

5 551738 37024936 441336 53798 26094 415442

Redvase 1 281347 27467966 430638 50610 147578 283060

2 208972 19712582 126146 38910 34322 91824

3 171916 15549446 16092 32724 - -

4 155254 13339766 263570 29266 33316 230254

5 85497 7965122 13764 31632 - -

Sunset 1 187173 15886682 233986 23470 87084 146902

2 122609 11922902 79828 7310 28504 51324

3 9203 9404646 170808 18802 64410 106398

4 73351 5364058 101826 10368 414170 60356

5 53444 2443382 153136 14308 64336 88800

Computer 1 81541 32786152 164660 74496 59914 104746

2 90819 26216200 437820 42390 171364 266456

3 162975 19402206 100656 25782 36534 64122

4 227673 14262004 66244 32700 16586 49658

5 124651 10566972 93566 43546 54990 38576

174

Bus_stop 1 68532 41698812 602986 57992 120210 482776

2 224366 35150354 317942 39084 103870 214072

3 255324 27054026 408640 56158 139434 269206

4 14964 20211811 471828 49188 149468 322360

5 139600 16105394 319284 61568 116218 203066

Car 1 337986 38392018 244458 46220 83682 160776

2 259028 31796600 492948 64728 168200 324748

3 281677 26225072 606096 51094 203744 402352

4 149093 20211040 469792 57222 159158 310634

5 31442 14965754 252890 31562 50534 202356

Cones 1 382208 38272864 346168 59854 103544 242624

2 176926 31858060 416000 36444 141430 274570

3 273172 24559804 386208 56744 129976 256232

4 38920 18909492 147464 56568 48734 98730

5 118793 15845126 292432 48700 79736 212696

Cycle 1 339224 33361488 558460 59946 130096 428364

2 268545 28638924 393224 46158 80130 313094

3 89348 22095454 386786 44770 60686 326100

4 154282 16718910 326948 41200 57396 269552

5 72995 13622194 208252 29120 67990 140262

Cycle_

board

1 272445 52658276 410142 60756 134896 275246

2 347667 44122398 591540 63606 185674 405866

3 234238 36312872 437594 69644 147804 289790

4 281095 30067236 479516 58212 162796 316720

5 166042 24636578 518752 60542 147764 370988

Cycle_ track 1 369659 40134452 528886 67754 175362 353524

2 13870 29477768 334938 42168 105774 229164

3 251285 24063312 534204 68662 183290 30914

4 101945 16848498 275922 45898 97666 178256

5 125068 13125848 222484 27188 90068 132416

Entry 1 299943 36529900 483466 68952 240398 243068

2 155866 29699606 472512 52008 152806 319706

3 227142 24344948 292892 39156 78420 214472

4 268729 18197094 276288 45508 76388 199900

5 180031 14564916 239250 33776 72296 166954

Info_ board 1 119064 31087052 526902 72260 112778 414124

2 57601 22999096 518262 52618 89364 428898

175

3 128924 17475194 184384 21960 29932 154452

4 37056 15380438 225750 26844 28368 197382

5 61220 11665028 119704 16688 7764 111940

Keep_left 1 448783 37360472 311790 29500 69330 242460

2 424765 29804804 635118 59122 157496 477622

3 213667 24002190 356026 56038 117170 238856

4 84931 21152046 258196 43918 39920 218276

5 187002 17204492 354920 52854 143342 211578

Left 1 372401 38769320 382476 60590 39476 343000

2 426569 32027590 326592 37278 83810 242782

3 380517 26169336 385742 36490 43752 341990

4 300754 20768620 280892 37008 46484 234408

5 150898 17111334 169482 18534 4236 165246

Main 1 449047 42393724 549156 70128 158746 390410

2 274168 35379770 436174 47596 163244 272930

3 190734 28081622 333080 41102 111264 221816

4 40307 21223152 168274 32492 62908 105366

5 209476 15660422 404532 61166 75376 329156

Median 1 543813 30207370 533302 61126 167018 366284

2 225214 23921270 279876 52274 123574 256302

3 270744 18098224 285616 40250 79384 206232

4 140512 14101626 211650 33798 70552 141098

5 87360 10653766 197868 39876 87292 110576

Yellow_

board

1 311037 37817482 549262 55122 207858 341404

2 327172 28346638 323232 43178 110858 212374

3 222770 23944916 422312 52596 172514 249798

4 77714 17271522 372796 37616 144684 228112

5 124197 13305054 425844 43878 147164 278680

Yellow 1 106413 32276878 244932 31284 67924 177008

2 109929 26959920 186476 19642 32870 153606

3 16506 22040176 333204 43024 118176 215028

4 44771 17202374 90012 14454 32170 57842

5 27754 14449966 49440 8988 21672 27768

Way 1 214907 24717680 140142 31928 52192 87950

2 152070 17936072 158438 26954 62192 96246

3 133349 12342358 143022 23326 55592 87430

4 91122 8870146 145472 23320 51728 93744

176

5 43926 5586664 10004 24022 - -

Vehicle 1 251825 41825172 416678 36770 143578 273100

2 337009 35355188 385340 66490 113794 271546

3 123982 29857654 266892 32880 60056 226836

4 189892 22448310 394136 28973 134516 259620

5 145032 19294014 374392 54826 146098 228294

Stop 1 319334 34383862 355648 35832 122426 233222

2 194661 26667954 378724 31128 115876 262848

3 142364 19198316 426406 55476 126882 299524

4 108150 15732282 265832 33708 101102 164730

5 143118 11533836 421846 33204 134100 287746

School_

ahead

1 504007 51985162 776784 78952 226472 550312

2 519831 44618884 666412 59082 202420 463992

3 294980 36346770 566132 61878 185188 380944

4 290240 30476032 484242 61280 89494 394748

5 202304 22931358 542018 67354 180386 361632

Round 1 344365 35985346 279348 53138 55344 224004

2 284315 30882064 212704 29968 16542 196162

3 32305 23234120 474814 47950 125034 349780

4 225765 16133362 243375 39724 59802 183570

5 202505 11707836 308230 46380 67596 240634

Road_sign 1 510434 35290920 376082 64924 120768 255314

2 270878 29097424 431984 46374 127408 304576

3 367177 20810828 301836 38028 89596 212240

4 34199 15756958 390478 37798 145964 244514

5 117960 12806294 356104 45520 105260 250844

Road_

board

1 276395 33606234 372190 59826 129136 243054

2 121994 26427836 415490 59046 123886 291604

3 201269 20972100 256322 32576 96212 160110

4 228059 16669642 123576 32634 37772 85804

5 197922 12305676 335422 47292 119992 215430

Road 1 343345 30021506 324228 65246 106758 217470

2 93140 24875848 357392 45556 136792 220600

3 51605 18511612 344724 45664 23568 321156

4 118608 11973422 359876 49508 53200 306676

5 72296 8149478 267792 35664 98084 169708

177

Right 1 540474 59912344 791448 89414 219392 57151

2 337959 46021064 770736 78236 238056 53268

3 454575 35688544 767614 85046 288792 478822

4 51674 28318848 237882 38150 43228 194054

5 170051 23018130 658578 90034 211962 446616

Over 1 365986 23366790 542086 56428 158056 384030

2 206828 17615318 436584 51100 98336 338248

3 173608 14873240 109786 15204 18990 90796

4 102171 11054664 415598 50050 133494 282104

5 6104 6311328 77632 15220 21480 56152

Nopark 1 618336 51252204 557778 45606 89398 469380

2 262741 42823566 402972 51280 90792 312180

3 169449 37692350 694002 57862 200338 493664

4 218660 30691400 613690 56338 152750 460940

5 86400 24554190 564000 51828 165880 398120

No_park 1 593042 44464144 561730 56196 160356 401374

2 301017 37692386 348610 52734 21212 327398

3 348848 28760400 385720 52244 170964 214764

4 183233 22319514 345720 38332 78716 267004

5 96408 17705950 543938 72855 59384 454554

No_entry 1 543571 44116832 563762 62676 186220 377542

2 309591 3659210 296156 38910 79426 216730

3 401343 30708476 674696 90730 249538 425158

4 165340 23913792 318072 35452 86688 231384

5 125541 19469546 617526 95196 195692 416834

178

D.3 Human Survey Result of various Test Images

1 2 3 4 5

Figure D.6: Sample Survey 1 with its image and 5 salient regions.

Table D.2 Survey Results of Figure D.6

The hierarchy of saliency identified by the system and the experts and the percentage of

results matched

Priority Case

1

Case

2

Case

3

Case

4

Case

5

Case

6

Case

7

Case

8

Case

9

Case

10

Result

%

1st

region 1 1 1 1 3 1 1 2 1 1 80

2 nd

region 3 3 3 3 2 2 2 3 2 2 50

3 rd

region 2 2 2 2 1 3 3 1 3 3 40

4 th

region 4 4 4 4 5 4 4 5 4 4 80

5 th

region 5 5 5 5 4 5 5 4 5 5 80

Cumulative result 66

1 2 3 4 5

Figure D.7 Sample Survey 2 with its image and 5 salient regions.



results matched

Priority Case

1

Case

2

Case

3

Case

4

Case

5

Case

6

Case

7

Case

8

Case

9

Case

10

Result

%

1st

region 1 1 1 1 4 1 1 1 1 1 90

2 nd

region 2 2 2 2 1 2 2 2 2 2 90

3 rd

region 4 4 4 3 3 4 3 3 5 3 50

4 th

region 3 3 3 4 5 3 4 4 4 4 50

5 th

region 5 5 5 5 2 5 5 5 3 5 80


179

1 2 3 4 5




results matched

Priority Case

1

Case

2

Case

3

Case

4

Case

5

Case

6

Case

7

Case

8

Case

9

Case

10

Result

%

1st

region 1 1 1 1 1 1 1 1 1 1 100

2 nd

region 2 2 2 2 2 2 2 2 2 2 100

3 rd

region 3 3 5 5 3 3 4 3 3 5 60

4 th

region 4 4 2 4 5 5 3 5 5 4 40

5 th

region 5 5 4 3 4 4 5 4 4 3 30


1 2 3 4 5




results matched

Priority Case

1

Case

2

Case

3

Case

4

Case

5

Case

6

Case

7

Case

8

Case

9

Case

10

Result

%

1st

region 1 1 2 2 3 5 1 2 2 1 40

2 nd

region 2 5 1 1 2 2 2 1 5 2 50

3 rd

region 5 3 5 3 1 3 3 3 1 3 60

4 th

region 3 2 3 5 4 1 4 4 4 5 40

5 th

region 4 4 4 4 5 4 5 5 3 4 30


180

1 2 3 4 5

Figure D.10 Sample Survey 5


The hierarchy of saliency identified by the system and the experts and the

percentage of results matched

Priority Case

1

Case

2

Case

3

Case

4

Case

5

Case

6

Case

7

Case

8

Case

9

Case

10

Result

%

1st

region 1 1 1 1 1 1 1 1 5 1 90

2 nd

region 2 2 2 2 2 2 2 2 2 2 100

3 rd

region 3 4 4 4 3 3 4 5 1 3 40

4 th region 4 5 5 3 5 4 3 4 4 4 50

5 th

region 5 3 3 5 4 5 5 3 3 5 50


1 2 3 4 5


Table D.7 Survey Results of Figure D.11 The hierarchy of saliency identified by the system and the experts and the

percentage of results matched

Priority Case

1

Case

2

Case

3

Case

4

Case

5

Case

6

Case

7

Case

8

Case

9

Case

10

Result

%

1st

region 1 1 1 1 1 1 1 1 1 1 100

2 nd

region 2 2 2 2 2 2 2 2 2 2 100

3 rd

region 3 3 3 3 3 3 3 3 3 3 100

4 th region 4 4 4 4 4 4 4 4 4 4 100

5 th

region - - - - - - - - - - -


181

D.4 Sample Feature Values for Sign Board Images

The following tables show the feature values for all the 11(Saliency, Color

conspicuity,Intensity conspicuity, Orientation, conspicuity, RG, BY, Intensity,

Orientation for 4 angles) feature of various test images in the order of decreasing

saliency.

Table D.8 Feature Values for signboard Application Name Order Salient C_color C_int C_ori RG BY Intensity Ori_0° Ori_45° Ori_90° Ori_135°

Boat.jpg 1 2635 14288 2874 12436 1435 12856 3918 4924 3368 2846 3347

2 2635 14288 2874 12436 1435 12856 3918 4924 3368 2846 3347

3 2380 15429 3368 12927 647 14784 4575 5365 3158 2623 4260

4 2380 15429 3368 12927 647 14784 4575 5365 3158 2623 4260

5 2210 16488 2435 10554 793 15696 3225 4752 2703 1786 2775

Butter.jpg 1 1700 13521 2357 10361 13521 0 3077 3256 2718 3011 3344

2 1700 13521 2357 10361 13521 0 3077 3256 2718 3011 3344

3 1615 12409 2221 8533 12409 0 2982 2368 2213 3078 2806

4 1530 10407 2211 8536 10407 0 2574 2666 2301 2641 2855

5 1360 8741 2199 7533 8740 0 2932 2573 2074 2352 2666

Nopark.jpg 1 2210 7758 2645 10860 7 7314 4077 2678 2457 4869 3203

2 1870 13365 2445 9842 10 13165 4016 3050 2444 3929 2183

3 1700 5500 2077 9408 50 5258 3308 2916 2435 3319 2348

4 1615 6522 2735 8749 74 6354 4044 2698 2397 3643 1768

5 1530 15354 1872 7991 757 14471 2750 2773 2111 3498 1980

School_ahead.jpg 1 1870 4563 3067 9996 2190 2215 4302 1549 2423 4694 3405

2 1785 5845 3113 9499 2806 2881 4564 1736 2802 3884 2910

3 1615 3908 2512 8918 2149 1627 3528 1858 2862 4448 3048

4 1445 4846 2559 8619 2932 1786 3768 1910 3034 4030 2851

5 170 550 1802 4866 513 0 2632 1735 1291 1776 1370

Shapes.jpg 1 3060 3549 3802 10715 0 3549 4885 3358 3653 4592 4273

2 2975 2771 3085 10740 0 2771 4054 3333 3869 4496 4768

3 2635 1349 1578 8289 0 1349 2084 2594 3121 3305 3313

4 2550 1547 1670 8180 0 1547 2230 3172 3224 2912 3272

5 2295 2006 2533 8491 0 2006 3297 2055 2076 4026 4185

Balloons.jpg 8755 27265 712 1899 20 25520 1169 692 590 549 615

7820 25576 1331 4510 1793 22332 2168 2072 1258 1411 1635

3995 17085 1707 5802 6004 10793 3110 2405 1622 1771 1951

3995 17085 1707 5802 6004 10793 3110 2405 1622 1771 1951

2380 17394 657 5323 2820 14513 847 1839 1584 1859 1930

bike_1.png 1 1615 20595 1488 8980 0 20595 2210 2532 2880 3003 2483

2 1275 13108 1737 8536 17 13082 2506 2182 2426 2549 2026

3 1190 9085 1659 8190 0 9085 1908 2421 2477 3236 2298

4 1190 9085 1659 8190 0 9085 1908 2421 2477 3236 2298

5 1190 9085 1659 8190 0 9085 1908 2421 2477 3236 2298

bike_2.png 1 2975 11958 2209 12883 0 11958 2912 4443 3702 4681 3015

2 2380 7716 2193 10899 34 7672 2418 2394 3006 5576 2830

3 2295 22277 1709 10254 0 22277 2678 2542 3566 5256 2992

4 1530 12795 3489 11089 0 12795 5152 2825 2888 4093 3818

5 1020 6632 2764 9973 15 6614 3680 3045 3034 3003 3272

bike_3.png 1 2635 6042 2998 13041 343 5705 3307 4675 4075 3696 4115

2 2295 8415 2808 11657 81 8330 3730 6077 3677 3564 3184

3 2125 16161 1704 9265 249 15882 2743 2939 2802 4044 2614

4 1955 16115 2579 10405 4 16117 3494 3847 3737 3289 3182

5 1785 5435 2078 9947 439 4987 3033 2483 2559 4577 3022

bike_4.png 1 1870 14306 2875 9048 63 14251 3307 3530 2580 3112 2355

2 1870 14306 2875 9048 63 14251 3307 3530 2580 3112 2355

3 1445 20392 3616 8744 25 20359 5241 3459 2143 2871 3062

4 1360 20081 3041 8469 191 19885 4353 3468 2765 3260 2565

5 1105 25162 2933 6606 38 25122 4742 1596 1264 2744 2301

bike_4.png 1 2465 19336 2730 11462 2 19335 3846 2853 3875 5601 3216

2 2040 23795 2307 10754 2 23794 3461 2581 3822 4963 2791

3 1955 3840 3089 9756 21 3828 3123 3697 2635 3104 2532

4 1955 3840 3089 9756 21 3828 3123 3697 2635 3104 2532

182

Name Order Salient C_color C_int C_ori RG BY Intensity Ori_0° Ori_45° Ori_90° Ori_135°

5 1615 6987 2473 9792 3 6983 2367 4466 2755 2683 2734

bike_5.png 1 1615 3182 2619 9548 73 3066 3236 4211 2278 1594 2891

2 1530 3860 2429 8601 156 3659 3064 3307 2443 1806 2729

3 1190 9347 1368 6594 1070 8214 1653 2527 2127 1728 1773

4 1105 5191 1340 6757 1179 3947 1765 2234 2592 1875 1649

5 1105 5191 1340 6757 1179 3947 1765 2234 2592 1875 1649

cross_1.png 1 1275 1727 1958 8560 829 852 2430 1991 2313 3318 2403

2 1275 1727 1958 8560 829 852 2430 1991 2313 3318 2403

3 1105 1338 1104 6994 1013 285 1533 1266 1680 3107 2081

4 1105 1338 1104 6994 1013 285 1533 1266 1680 3107 2081

5 1020 2399 1059 7188 2056 293 1444 1345 1651 2998 2230

cross_2.png 1 2125 5004 3190 10434 1645 3374 5059 5202 2568 2205 3114

2 1870 16446 2180 10127 1465 14935 2813 3402 2741 3624 3108

3 1445 8917 1981 10432 1291 7572 2456 3969 2877 3613 2796

4 1325 3692 1967 7271 3455 212 2873 2468 1793 2667 1984

5 1275 7289 1572 6802 40 7216 2695 1480 1612 3244 2024

cross_3.png 1 2975 9655 2536 12428 1603 8061 3200 6389 2742 2569 3408

2 2535 8875 2330 12229 1054 9810 4656 5940 2976 3622 3320

3 2380 8516 1842 10926 594 7935 1894 5398 2555 2906 2738

4 2125 14716 1828 8809 829 13821 3219 2594 3046 4035 3288

5 1870 17921 1204 6997 499 17459 1432 3876 1586 1199 1998

pedes_1.png 1 1275 9207 1642 7346 222 8965 2065 2346 2888 3016 1710

2 1190 4919 2052 8495 545 4296 2768 2966 2256 2953 2250

3 1105 3284 1353 3242 234 3284 2771 1283 1010 1032 1118

4 935 2876 2040 6836 196 2640 2797 2621 1803 2206 2063

5 850 2843 1722 7070 720 2048 2340 2501 1760 2496 1739

pedes_2.png 1 2380 11433 3140 11023 283 11149 3329 4567 3112 4644 3452

2 2295 16995 3279 10471 0 16995 3594 2805 4241 5925 2665

3 2210 12517 2572 10686 10 12505 2276 3448 2743 4426 3444

4 2210 12517 2572 10686 10 12505 2276 3448 2743 4426 3444

5 2210 12517 2572 10686 10 12505 2276 3448 2743 4426 3444

pedes_3.png 1 1870 8551 2700 10435 0 8551 2386 2366 3169 5192 3424

2 1870 8551 2700 10435 0 8551 2386 2366 3169 5192 3424

3 1275 4560 1918 7329 0 4563 1647 1753 2360 3717 2164

4 1275 4560 1918 7329 0 4563 1647 1753 2360 3717 2164

5 1190 8258 2174 8036 0 8258 2551 2917 2522 2948 2170

pedes_6.png 1 10370 30080 1538 5422 548 29532 3066 718 1504 3069 1423

2 9435 29523 1563 5769 319 29205 3368 696 1656 3312 1442

3 8245 29347 1675 4349 569 28778 3326 1610 1157 1441 1572

4 7735 28080 2449 6022 373 27710 4140 1963 1905 2836 2161

5 1785 10063 3807 8156 403 9653 6016 3725 2757 2065 1690

pedes_5.png 1 2550 4602 1887 11680 133 4459 2655 4058 2998 3996 2687

2 2465 9965 3108 10370 5 9967 4851 5148 2559 2684 2973

3 2380 6742 2592 10535 118 6596 3717 4536 2863 3125 3169

4 2550 4602 1887 11680 133 4459 2655 4058 2998 3996 2687

5 2040 12153 2357 10167 25 12148 3682 3871 2722 4155 2869

pedes_6.png 1 11135 28226 3668 7064 569 27662 6242 2338 2222 3013 2642

2 7565 19110 4260 13500 819 18322 5529 4641 5582 4836 3582

3 6630 25673 1775 4972 255 25421 3164 960 1665 2878 1781

4 6205 16933 2872 6418 1085 15854 5277 878 3073 2982 1344

5 2380 13071 4300 12245 130 12949 6039 3616 3975 5839 4387

car_1.png 1 2720 12957 1959 9957 35 12906 3267 4367 2756 2552 3168

2 2380 11797 2337 9212 0 11797 3717 4350 2578 2646 2991

3 2295 15296 2868 11490 0 15296 4403 3509 3117 5271 4686

183

4 2210 8485 1375 7821 61 8406 1926 3789 2360 1617 2461

5 1955 10904 1456 7791 38 10850 2092 3488 1763 2264 2681

car_2.jpg 1 2635 18567 3728 7647 305 18224 6203 1672 3122 4069 2086

2 2040 27228 2122 3353 98 27132 4296 1131 1151 1156 987

3 1615 23571 1740 5445 19 23555 3397 1766 2051 2483 1680

4 1615 23571 1740 5445 19 23555 3397 1766 2051 2483 1680

5 1530 12034 2353 8470 685 11318 3787 2026 2379 3685 2067

car_3.png 1 2890 5149 1523 11381 25 5110 1942 2938 3153 4982 3126

2 1870 4424 473 6997 0 4425 623 1718 2031 3263 2086

3 850 682 2395 6605 2 515 3423 1468 1105 2542 3886

4 595 942 1713 4749 11 820 2653 892 778 1750 2941

5 595 942 1713 4749 11 820 2653 892 778 1750 2941

car_4.png 1 2125 1235 2288 9320 0 1235 2621 1990 1788 4042 3793

2 1870 1260 1793 8130 0 1220 2291 1845 1526 3125 3407

3 1870 1260 1793 8130 0 1220 2291 1845 1526 3125 3407

4 1785 11028 1405 7746 33 10988 2602 2314 2643 3823 2416

5 1615 8673 1816 8267 1 8671 2671 2439 1562 3494 3020

car_5.png 1 2210 4982 2807 10039 1625 3277 3652 3413 2990 3466 2170

2 2125 4236 2857 10643 924 3309 4031 3382 3280 4365 3358

3 765 1682 2147 7204 66 1573 2974 2036 1986 3006 2319

4 1955 4983 2476 9234 1362 3604 3061 3513 2977 2670 2263

5 1870 2914 2218 9307 649 2067 3404 2903 2913 3446 2374

car_6.png 1 425 1682 2274 7295 63 1592 3074 1999 1997 3153 2300

2 1955 4907 3000 9734 2016 2909 3712 3190 3250 3277 2050

3 1955 4907 3000 9734 2016 2909 3712 3190 3250 3277 2050

4 1785 4851 2578 9215 1928 2925 2974 3801 3245 2708 1962

5 1785 4851 2578 9215 1928 2925 2974 3801 3245 2708 1962

184

D.5 Sample Results of Visual Attention Model and Saliency

Toolbox for Various Images

The most five salient regions of the image are segmented and shown in order

along with the result of saliency toolbox for each image.

a)

b)

c)

d)

e)

Figure D.12. Sample results of visual attention model and saliency toolbox

result for various images (a-e)

185

a)

b)

c)

d)

e)

Figure D.13: Sample results of visual attention model and saliency toolbox

result for various images (a-e).

186

D.6 Training and Testing Datasets for Tracking the Vehicle

The training and testing datasets for identifying the car is given in Table D.9.

The RG, BY, Int, Ori 0°, Ori 45°, Ori 90°, Ori 135° represents Red-Green and Blue-

Yellow Feature maps, C_in,C_col, C_Ori represents the Intensity, Color and

Orientation Conspicuity maps, SM represents Saliency Map and Class represent the

Category to which the vehicle belongs. 1 represents Vehicle class and 2 represents

non vehicle class.

187

TABLE D.9 Sample dataset for tracking the vehicle

RG BY C_c lnt C_int Cri_0º Cri_45º Cri_90º Cri_135º C_ori SM Class

0.6829 0.1458 0 0.0847 0 0.8773 0.7229 0.5159 0.4775 0.506 0.1687 1

0.6749 0.1789 0 0.0847 0 0.8562 0.7781 0.4904 0.4974 0.506 0.1687 1

0.6531 0.1796 0 0.0423 0 0.8598 0.7725 0.4749 0.4993 0.4216 0.1405 1

0.631 0.2364 0 0.0423 0 0.9087 0.8585 0.4735 0.5556 0.5903 0.1968 1

0.6154 0.2136 0 0 0 1.0377 0.8423 0.5589 0.5403 0.6746 0.2249 1

0.5354 0.2761 0 0 0 1.1911 1.0284 0.5992 0.5903 0.7589 0.253 1

0.5116 0.3032 0 0 0 1.1584 0.9659 0.5387 0.5556 0.9276 0.3092 1

0.506 0.2837 0 0.0847 0 1.1604 0.9448 0.5813 0.5638 0.9276 0.3092 1

0.4765 0.3604 0 0.1693 0 1.1104 0.9454 0.756 0.5678 1.0119 0.3373 1

0.4686 0.3261 0 0.0847 0 1.1204 0.9752 0.6104 0.5612 1.0119 0.3373 1

0.4279 0.3459 0 0.0423 0 1.3142 1.0245 0.6369 0.626 1.1806 0.3935 1

0.4064 0.4451 0 0 0 1.3846 0.9673 0.7312 0.6071 1.3492 0.4497 1

0.3919 0.457 0 0 0 1.4441 1.0377 0.581 0.6448 1.2649 0.4216 1

0.369 0.4924 0 0.0423 0 1.3747 1.0394 0.626 0.708 1.0962 0.3654 1

0.3595 0.6108 0 0.0423 0 1.4438 1.1597 0.7116 0.7629 1.6022 0.5341 1

0.335 0.6878 0 0.0423 0 1.2202 1.1263 0.7907 0.8224 1.5179 0.506 1

0.3244 0.5873 0 0 0 1.2315 0.955 0.7341 0.7573 1.3492 0.4497 1

0.3479 0.6928 0 0.0423 0 1.2834 0.957 0.7973 0.7212 1.5179 0.506 1

0.3416 0.5731 0 0.0423 0 1.3694 1.2321 0.628 0.6247 1.2649 0.4216 1

0.293 0.7588 0 0.051 0 1.5963 1.3547 0.7966 0.8316 2.5378 0.8459 1

0.3704 0.5734 0 0 0 1.2285 0.9511 0.8396 0.746 1.4335 0.4778 1

0.3836 0.5033 0 0 0 1.1233 1.0701 0.7166 0.668 1.2649 0.4216 1

0.1137 1.2766 0 0 0 1.8996 1.6752 2.6373 2.1609 1.3064 0.4355 2

0.287 0.5498 0 0 0 1.4172 1.3658 0.8742 0.9021 2.1318 0.7106 1

0.3661 0.4745 0 0 0 1.211 0.8925 0.7731 0.751 1.6865 0.5622 1

0.3922 0.6979 0 0 0 1.2728 0.9476 0.7757 0.6606 1.4297 0.4766 1

0.1742 1.2508 0 0 0 1.8268 1.7285 2.9078 2.1957 1.0451 0.3484 2

0.1732 1.2613 0 0 0 1.8391 1.7203 2.8781 2.1711 1.0451 0.3484 2

0.1762 1.2592 0 0 0 1.832 1.7131 2.8658 2.1588 1.0451 0.3484 2

188

RG BY C_c lnt C_int Cri_0º Cri_45º Cri_90º Cri_135º C_ori SM Class

0.1855 1.2561 0 0 0 1.8709 1.7295 2.917 2.2018 1.0451 0.3484 2

0.6544 0.0615 0 0 0 0.6987 0.6121 0.6121 0.5394 0.506 0.1687 2

0.5987 0.8714 0 0 0 1.0566 0.703 0.5816 0.6599 1.3421 0.4474 1

0.3895 0.6224 0 0 0 1.031 0.6356 0.7784 0.7447 1.2615 0.4205 1

0.3813 0.6827 0 0 0 1.033 0.8407 0.7998 0.7249 1.3456 0.4485 1

0.3559 0.6431 0 0 0 1.1144 0.7335 0.7892 0.8516 1.1774 0.3925 1

0.7857 0.002 0 0 0 0.6796 0.5651 0.5407 0.412 0.253 0.0843 2

0.7804 0.002 0 0 0 0.6819 0.5731 0.4868 0.3717 0.0843 0.0281 2

0.7821 0.0023 0 0 0 0.6911 0.5493 0.4861 0.373 0.0843 0.0281 2

0.5257 0.9043 0 0 0 0.949 0.7885 0.8243 0.8086 1.3421 0.4474 1

0.7864 0.002 0 0 0 0.7011 0.5446 0.4722 0.3704 0.0843 0.0281 2

0.9933 0.9312 0 0 0 1.021 0.998 1.2544 0.7674 1.7148 0.5716 1

0.168 1.1977 0 0 0 1.8361 1.7561 2.8996 2.2602 1.5676 0.5225 2

0.5132 0.8757 0 0 0 1.0097 0.8141 0.649 0.7003 1.3421 0.4474 1

0.5286 0.9296 0 0 0 1.0661 0.9411 0.822 0.7849 1.426 0.4753 1

0.1568 1.2121 0 0 0 1.8484 1.7295 2.917 2.2305 1.3064 0.4355 2

0.1557 1.21 0 0 0 1.8668 1.7285 2.9252 2.2182 1.5676 0.5225 2

0.7817 0.002 0 0 0 0.708 0.5585 0.4719 0.3806 0.0843 0.0281 2

0.7864 0.8156 0 0 0 0.9065 0.7477 0.7733 0.5922 1.3386 0.4462 1

0.7324 0.7049 0 0 0 1.102 0.8888 0.6763 0.6984 1.5938 0.5313 1

0.8128 0.0023 0 0 0 0.7275 0.5724 0.496 0.3968 0.1687 0.0562 2

1.0484 5.9435 0 0 0 1.996 2.2681 3.5212 2.619 3.5988 1.1996 2

0.7126 0.8911 0 0 0 0.9531 0.8114 0.7844 0.8219 1.5059 0.502 1

0.1701 1.2766 0 0 0 1.8371 1.7777 2.9949 2.1834 1.3064 0.4355 2

0.687 0.8609 0 0 0 1.1407 0.9482 0.8192 0.7677 1.5896 0.5299 1

0.6965 0.8442 0 0 0 1.0292 0.8094 0.7569 0.7694 1.3386 0.4462 1

0.1557 1.29 0 0 0 1.8391 1.7572 2.9631 2.2018 1.3064 0.4355 2

0.1537 1.3125 0 0 0 1.7971 1.7705 3.0502 2.2398 1.3064 0.4355 2

0.7254 0.9291 0 0 0 1.0866 1.0184 0.8848 0.7365 1.6732 0.5577 1

0.77 0.7752 0 0.0838 0 0.8429 0.9152 1.0589 0.6296 1.2516 0.4172 1

0.7912 0.7608 0 0.0838 0 0.8832 0.9653 0.9925 0.6708 1.4185 0.4728 1

189

APPENDIX A THE NEURO BIOLOGY OF VISION AND ATTENTION A.1

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