A Performance Analysi s of Various Feature …A Performance Analysi s of Various Feature Detectors...

A Performance Analysis of Various Feature Detectors and their Descriptors for

Panorama Image Stitching

ISRAA HADI ALI1 and Sarmad Salman

2

1IT College. Babylon University, Iraq

[email protected] 2Msc. student - IT College. Babylon University, Iraq

[email protected]

Abstract

Feature detectors and descriptors play an essential role in computer vision application such as

image registration, object recognition, and image classification and retrieval. This paper presents the

analysis of the performance of multiple feature detectors and descriptors, namely SIFT, SURF, ORB,

BRIEF, BRISK, FREAK. It analyzed in terms of the number of features, the number of matching

points in overlapping regions between images and subjective accuracy of stitching. Extraction of

different symmetric features from image increases the chance of the requirement reliable matching

among a variety of scene views. One result of the experiments shows that AGAST, FAST and

BRISK detectors have the highest number of detected key points, whereas STAR, AKAZE, and

MSER have a lower number of extracted key points. Moreover, the speed of each algorithm is

registered.

Keywords: Feature Detection, Feature Description, Panorama Image Stitching

1- Introduction

Feature detection is the process of extracting image information by searching at every point

and see if there is an image feature that gives the same style to existing feature types such as point,

line, corner, and blob. Feature detection methods and algorithms have a considerable contribution for

implementing many applications in computer vision such as feature matching, 3D scene

International Journal of Pure and Applied MathematicsVolume 119 No. 15 2018, 147-161ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

147

reconstruction, image retrieval, and object categorization. The salient features are entities that are

desirable and extended over the entire image [7]. It should be highly distinctive and exclusive, i.e. it

has the same illustration for each occurrence in the images. An optimal method of feature detection

must be vigorous to the transformations and the distortions of images like translation, rotation, scale,

change in intensity and firm to noise and compression [19]. Feature description is the process of

describing the interest points and creating a vector which includes all the information concerning

magnitude and direction. Feature descriptors can depend on one or more of the measures such as

second-order statistics, coefficients image transform and parametric models [12]. The size of

descriptor determines computation complexity, since if it is large, then it will arise long computation

time, but if it is small, then some useful information may be rejected [9].

The investigation of robust feature detectors and descriptors (SIFT, SURF, ORB, BRISK,

BRIEF, etc.) and knowing the differences between them is very important, in view of the fact that

there are tradeoffs in speed, execution time and accuracy among them.

Many comparative studies of feature detectors and descriptors have elaborated many aspects of

differences between them. Furthermore, the experiments presented provide more details about

specific characteristics of each detector and descriptor, such as the number of features detected, the

time complexities, the extent of the resistance to noise, change in illumination and variety of

transformations. In [1], authors have evaluated the performance of FREAK, SURF and BRISK

descriptors and many detectors against diverse types of constraints, specifically rotate, scale and

illumination. In [5], the writers have compared different contemporary key point detectors and

descriptors. Their experience calculated the mean and deviation of distances among feature

descriptors in low-good light status. Other assessments [2], [3], and [4] were presented results aiming

at the predilection of one or more combinations, or at identifying the detectors descriptors that offer

better performance under a specific field or application.

In this paper, the evaluation of the performance of multiple combinations of feature detection

and description techniques have been manipulated. The analysis includes comparing the number of

features extracted from each detector with all possible descriptors, comparing the matching points

between sets of images, in addition to register the time of features detecting.

The rest of this paper is recognized as follows: Section two presents a brief discussion of

feature detectors and descriptors. Section three is concerned with the analysis of the performance of

these techniques. Finally, Section four presents the conclusions arrived at from the study.

International Journal of Pure and Applied Mathematics Special Issue

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2- Features Detection and Description

The features that can be extracted from images are of two types, which are global and local

features[12]. Global features describe the image as a whole, perhaps it is an attribute such as the

color or texture included in all pixels. On the other hand, local features involve the key-point or the

interest region of the image, including the corner, blob, and edge. It is detected and described by the

local features detectors and descriptors. The robust feature detector should be invariant to most

image transformations such as affine transformation, and there must be resistance to noise presence

and blurring as well. It should extract any point or region with the corresponding location in order to

achieve the optimal matching of the same scene in multiple images. There are different feature

detectors and descriptors, as in the following:

1- SIFT Detectors and Descriptors

SIFT, the abbreviation of Scale Invariant Feature Transform, was proposed by Lowe et al. It

solved many image limitations on feature detection and description such as scaling, rotation, affine

transformation, intensity, noise, and viewpoint changes. The SIFT consist of four main steps, which

are scale space extreme detection, key-point localization, orientation assignment, and key-point

descriptor [2]. In the first step, SIFT uses ( DoG ) Difference of Gaussian, to estimate scale space

extreme. The DoG is computed for different octaves and layers per each octave of the image, figure

(1). The extreme is selected if it has the best representation compared with the current, previous and

next scales of (3×3) neighborhood, figure (2). The second step is key-point localization where

interest points are localized and refined by rejecting insignificant and low contrast points with

respect to the known threshold. In the third step, the orientation is assigned to each key-point by

choosing a neighborhood around the interest point location that depends on scale and the gradient

magnitude, and the direction is computed for this region as well. After that, the histogram is

constructed and the highest bin is selected as the representative for each block. The last step is the

key-point descriptor, where the feature vector is generated. A (16×16 ) block centroid with key-

point, SIFT splits it to 16 sub-region of (4×4) sizes. Each sub-region construct orientation histogram

with 8 bin. The length of the vector is (4×4×8=128 ) element. Descriptor vector must be robust to

change in illumination and viewpoint, rotation, compact, and highly distinctive [1,6].


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Figure 1: compute the DoG Figure 2: comparison pixels to extreme

detection

2- SURF Detectors and Descriptors

Speed-up Robust Feature, abbreviated as SURF, was proposed by Herbet Bay. It is a local

feature detector and descriptor used for registration, 3D reconstruction and object recognition. SURF

detector is inspired by the SIFT descriptor [2]. SURF is computationally faster than SIFT and it is

robust against image transformation and noise. It consists of two major steps: (1) Key point detection

(2) key point description. In the first step, SURF uses BLOB detector relying on the Hessian matrix

to extract the maximum element as interest point [8]. The assignment of orientation is performed by

using Haar wavelet responses that are calculated for the local region around each key point by

applying appropriate Gaussian weights. For key point description step, the integral image

conjugation with the Haar wavelet are used to encode the distribution of pixel intensity value. The

neighborhood around each key point is a circular region. It is divided into (4×4) sub-region, for each

sub-region computes the response of Haar wavelet, that contributes with four values to the

descriptor. Feature descriptor is represented with (4×4×4=64) dimension for each key point [5].

3- ORB Detectors and Descriptors

ORB, the Shortcut of Oriented FAST and Rotated BRIEF, was proposed by Rublee et al. It is

constructed on FAST key point detector and BRIEF descriptor after significant modifications to

improve the performance [13]. It is a fast descriptor, invariant to rotation and noise resistant. The

detection of the key point step is based on the modified FAST detector and it constructs the scale

pyramid of the image. First, interest points are detected, then sorting them by applying Harries corner

measure and elect N top points based on a threshold. The FAST does not compute the orientation, so

computed by first ordered moments to improve the rotation invariance. The standard BRIEF

descriptor is a poor rotation task, whose function is to manage this problem ORB computed rotation


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matrix using the patch orientation. This makes BRIEF descriptor steered according to the orientation

[7,10].

4- BRISK Detectors and Descriptors

Binary Robust Invariant Scalable Key point (BRISK), was proposed by Stefan Leutenegger et

al. It is a key point detector and descriptor inspired by AGAST and BRIEF [5]. It is developed to

overcome the obstacles with SIFT and SURF such as a large number of resources and high power

requirements. BRISK uses AGAST to detect the key point that is an improvement in the speed of

FAST with a similar performance for preserving detected features. Her detector employs the

Hamming distance to achieve low computation time. BRISK consists of three steps: (1) sampling

pattern, (2) orientation compensation and (3) sampling pairs [2]. In the first step, the sampling

pattern is a set of concentric points circles that spread around the interesting point, which determine

whether the point is a corner or not based on FAST or AGAST detector. Gaussian smoothing is

performed at each pixel sample point to get the point value. Then, these pairs are grouped in three

sets: (1) short-distance pairs, (2) long-distance pairs and (3) unused pairs (unused pairs are not

involved in both other sets) [8]. The second step is orientation compensation that gives rotation

invariance. The calculation of sum gradient between 'short pairs' and 'long pairs' determines the

direction of interest point. Finally, sampling pairs are achieved by comparing the intensity value

between the first and second points for all pairs. The result is the 512- bit binary descriptor, for

matching key point BRISK uses Hamming distance. This is done by comparing the sum of "XOR

operation" among two binary descriptors [2].

5- BRIEF Descriptor

BRIEF, the abbreviation of Binary Robust Independent Elementary Features, is a local binary

string descriptor proposed by Michael Calonder et al. [20]. It presents an efficient performance and

good accuracy in robotics application[9]. BRIEF builds a binary descriptor by comparing the

intensity values between two randomly selected pair of points in an image patch, which described

featured currently is centroid it. Simply, every bit in the feature descriptor appears as this, if the

intensity value of the first point is higher than the intensity value of the second point of the compared

pair, and reset otherwise. The BRIEF descriptor is sensitive to noise, so initial smoothing is

performed implement on 9×9 pixels filter. Hamming distance is used in lieu of Euclidian distance to

compute the similarity of the descriptor [12]. For this reason, the BRIEF descriptor is

computationally faster than other descriptors. The length of the descriptor is 256 bit and computes

over 31×31 size of patch pixels. It is robust to contrast and brightness, but not sturdy to the rotation.


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6- Freak descriptor

The freak ( Fast Retina Key point ) descriptor was proposed by Alexandre Alahi and et al.[5].

It biologically inspired by the human visual system, particularly the retina. It uses Retina sampling

grid which is circular where the points near from the center have a higher density. The intensity of

the points as it moves away from the center, it decreases exponentially. The image is smoothed with

different Gaussian kernel size to make every sample points more resistance to noise as in BRISK.

The FREAK descriptor reinforced feature orientation, it grouped the predestined local gradients over

selected point pairs. It adopted the ‘course-to-fine’ strategy to a description of features. FREAK

results a binary string descriptor like BRIEF. The saccadic search starts with the first ( 16 bytes ) of a

descriptor. The search continues when the distance is lower than a predefined threshold. This

approach rejects almost-candidate points. So, the computation time is shortened and the memory is

saved. The FREAK descriptor is robustness to contrast, rotate, scale, and blur[9].

7- FAST Detector

FAST (Features from Accelerated Segment Test), a technique was proposed by Rosten

Drummand. FAST is a corner detector developed to be faster than many detectors, so it used in real

time frame rate application [11]. FAST detectors draw 'BRESENHAM circle' around each point to

evaluate the circle of pixels to extract features [1]. This circle consists of 16 pixels (labeled from 1-

16) with a radius of 3. As observed in figure (3), the candidate point p is classified as a corner or not

according to two stages. The first stage is to compare the candidate point with the intensity value of

1,5,9 and 13 pixels if at least three pixels are brighter or darker than the candidate point against the

predefined threshold. Then, this point is classified as a feature and proceeds to the next stage,

otherwise, the candidate point is rejected. The second stage, where 3 points satisfy a given criterion

and then test all 16 pixels. In this stage, if 12 contiguous pixels satisfy the previous condition, then

the point is considered to be a feature. This process is repeated for all other points in the image.

Many problems have been manipulated to overcome the limitation and obstacles encountered with

using FAST. This approach includes machine learning, ID3 (a decision tree classifier) and non-

maximal suppression [12].


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Figure 3: corner detection by fast algorithm

8- MSER Detector

Maximally Stable Extremal Region (MSER), the detector was proposed by Matas et al. It is

effective to object recognition and achieves a better performance for stereo matching [9]. In images,

MSER is adopted as BLOB detection. It determines the association among image elements from two

images along with different viewpoints. The meaning of 'Extremal' indicates that each pixel in the

MSER region has lower or upper intensity, i.e. brighter or darker extremal regions, than all pixels on

outside region ambit [3]. In other words, MSER region consists of connected pixels unchanged over

a range of thresholds. MSER detection is analogous to a 'water shedding' process described in [2].

They divide the set of pixels into two sets, which are Black and Wight, according to the intensity of

the threshold. The 'Maximally Stable' region is one that maintains its state with only little change

over several intensity thresholds that are selected. It is invariant to affine transformation.

9 - Other Features Detector and Descriptors

- STAR key point detector is derived from 'CenSurE' feature detector [5]. It uses a bi-level

approximation of the LOG filter, the abbreviation for Laplacian of Gaussian.

- Harris Laplace is a corner detector proposed by Mikolajczyk and schimd [12]. It depends on a

composition modified copy of Harris corner detector and representation of Gaussian scale space. The

Harris-Laplace retrieves a considerably smaller number of points compared to the (LoG) or (DoG)

detectors. It is invariant to scale, but futile to affine transformation.

- GFTT, the abbreviation of Good Feature To Track, was proposed by Shi and Tomasi [18]. It is a

feature detector based on investigating the local autocorrelation function to the intensity of the

image.


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- KAZE algorithm was proposed by Alcantarilla et al. It detects and describes 2D features in non-

linear scale-space extrema. KAZE use non-linear diffusion filtering besides the Additive Operator

Splitting (AOS) method that makes blurring adaptive to image features. It follows the same main

four steps as SIFT for object recognition with a significant alteration[15].

- AKAZE (Accelerated KAZE) is created for the alleviation of the costly computation with the

implementation of KAZE[17]. It uses FED, an abbreviation of Fast Explicit Diffusion, a method to

arise nonlinear scale space. AKAZE employes the modified copy of LDB descriptor as a binary

descriptor to speed up the key-point description.

- LUCID, an abbreviation of Locally Uniform Comparison Image Descriptor, was proposed by

Zieyler et al. [16]. It is based on the linear time permutation distance among RGB ordered value of

two image patches. LUCID uses Hamming distance as a comparing method in the matching stage.

- LATCH, the abbreviation of Learned Arrangements of Three Patch Codes, was proposed by Levi

and Hassner [17]. It is a binary descriptor that uses a 3×3 patches for comparison. LATCH elects 3

patches, the first of which is named anchor, and then calculates the Frobenius distance between an

anchor and the other 2 pixels. After that, it compares both distances, learned and selects the best

patch triples.

The Experiment

The current study is concerned with the evaluation of the performance of a variety of

combinations of methods of detection and description. A comparison is made between each detector

and all the possible descriptors on it. The results are recorded, concerning the number of discovered

features and the time of detection, and the number of identical features between each detector and

descriptor is extracted.

The experiment is executed on a set of images taken from the Internet, which provides a

sufficient overlap between them to give the best number of detected features. These images are of the

following sizes: 384×512.


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Figure 4: The images of The Experiment

OpenCV library with Python language is used under the Windows 7 environment to complete

tests. The execution performed on a laptop holds the following specifications: Intel ® 4nd

generation

core i7 4600U 2.10 GHz 2.70 GHz processor, 8 GB RAM.

The number of features detected plays an important role in image stitching and the completion

of precise image panoramas. Perhaps, the greater the number of features detected leads to increasing

of matches between images, thereby providing a sufficient number of features that are finally used to

calculate the homography matrix.

The experiment aims to study the behavior of each detector of feature against a set of

descriptors and to know the number of features detected in each attempt. As shown in Tables 1,2,3

and 4 the results show that AGAST, FAST and BRISK give the largest number of features detected

with all the descriptors, while STAR, MSER, and AKAZE give the lowest number. It is worth noting

that some detectors offer a high detection rate. However, in the matching phase, they lose the greater

part of them. Although SIFT, SURF, and KAZE give a moderate number of features, they give the

highest rate of accuracy when stitching images. Tables 5 and 6 views the number of matching

features for all the comparisons that are shown in Tables 1,3 and 2,4 respectively.

Table 1: Shows the detected features for image 1

Descriptor SIFT SURF ORB BRISK BRIEF LUCID DAISY LATCH FREAK

Detector

SIFT 956 956 956 827 692 956 956 701 693

SURF 1943 1943 1943 1509 1703 1943 1943 1717 1047

ORB/1000 983 983 983 623 983 983 983 983 150

BRISK 2218 2218 2218 2218 2059 2218 2218 2074 1668

KAZE 986 986 986 835 821 986 986 726 679

AKAZE 507 507 507 507 507 507 507 507 507


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Descriptor BRISK BRIEF LUCID DAISY LATCH FREAK

Detector

FAST 3960 3312 4531 4531 3339 3492

STAR 166 166 166 166 166 202

HAR.LAP* 730 718 857 857 725 607

MSER 180 197 282 282 199 85

AGAST 4157 3436 4740 4740 3472 3640

GFTT 840 793 1000 1000 698 816

* HARRIS LAPLACE


Descript SIFT SURF ORB BRISK BRIEF LUCID DAISY LATCH FREAK

Detector

SIFT 941 941 941 857 762 941 941 768 760

SURF 2105 2105 2105 1609 1840 2105 2105 1860 940

ORB/1000 989 989 989 582 989 989 989 989 107

BRISK 2591 2591 2591 2591 2426 2591 2591 2447 1891

KAZE 1063 1063 1063 911 716 1063 1063 826 783

AKAZE 582 582 582 582 582 582 582 582 582



Detector

FAST 4517 3821 4990 4990 3870 4042

STAR 213 213 213 213 213 160

HAR. LAP. 773 716 998 998 734 587

MSER 126 139 218 218 140 85

AGAST 4640 3880 5147 5147 3930 4104

GFTT 910 692 1000 1000 797 724


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Figure (5) shows that the algorithm resulting in high-precision outcomes due to an abundance

of matching key points as in Table 5 or 6. Otherwise, when the number of matching key points is

Significantly little, then the results are like figure (6).

Figure 5: Left image is created by KAZE detector and SIFT descriptor. Right image is created by

AGAST detector and BRISK descriptor.

Figure 6: Left image is created by HARRIS LAPLACE detector and DAISY descriptor. Right image

is created by ORB detector and LATCH descriptor.

Table 5: Illustrates the matching features between image 1 and image 2 (table 1 & 3)

Descriptor SIFT SURF ORB BRISK BRIEF LUCID DAISY LATCH FREAK

Detector

SIFT 219 219 219 91 73 205 172 73 61

SURF 441 441 441 191 215 440 467 138 56

ORB/1000 81 81 81 72 89 154 73 64 no

BRISK 228 228 228 228 316 429 427 150 117

KAZE 277 277 277 197 132 260 260 91 90

AKAZE 100 100 100 88 102 157 150 64 60

Table 6: Illustrate the matching features between image 1 and image 2 (table 2 & 4)


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Detector

FAST 407 486 821 834 364 240

STAR 11 23 69 24 12 10

HAR.LAP. 5 18 292 68 7 no

MSER 25 27 69 29 19 10

AGAST 379 469 837 747 312 235

GFTT 162 157 201 286 142 97

* HARRIS LAPLACE

Experiments show that using the KAZI and AKAZI algorithm as descriptors when combined

with all detectors gives results largely consistent with the behavior of SIFT and SURF descriptors, in

terms of the number of detected features, the number of identical points and the accuracy of

stitching. So, their results have been deleted from the tables. Furthermore, LUCID and DAISY give

the same results as SIFT with all detectors. The only case in which we did not get the matching

points were between the FREAK descriptor and each of ORB and HARRIS LAPLACE detector.

In summary, we refer in Table 7 to the Time spent to discover the features for all detectors with

respect to each of the BRISK, and FREAK descriptor.

Table 7: shows the time of detect features in milliseconds.

Detectors SIFT SURF ORB BRISK FAST STAR KAZE AKAZE HARRIS MSER AGAST GFTT

BRISK

Img1 0.053 0.043 0.013 0.033 0.001 0.005 0.237 0.028 0.390 0.109 0.005 0.007

Img2 0.055 0.052 0.006 0.037 0.002 0.006 0.246 0.029 0.406 0.093 0.006 0.008

FREAK

Img1 0.052 0.053 0.013 0.032 0.001 0.005 0.226 0.027 0.247 0.115 0.005 0.008

Img2 0.055 0.048 0.006 0.040 0.002 0.005 0.228 0.029 0.247 0.114 0.006 0.007

Conclusion

Many combinations of image feature detectors and descriptors are used to optimize the

outcomes that result from them. Conversely, the comparisons have been discussed to cover the most

properties and points of weakness and robustness among these techniques. In this study, there are

comparisons between all features detectors and possible descriptors, to make the results of features

detection under each descriptor concise. The experiment shows that BRISK, BRIEF, and LATCH


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have an effect on the number of features detected from SIFT, SURF, ORB, BRISK, KAZY, and

AKAZE detectors, whereas other descriptors did not have an effect. In other words, they give the

same result under all detectors. What's more, only DAISY and LUCID descriptors give similar

results against FAST, MSER, HARRIS LAPLACE, AGAST, GFTT detectors. Some detectors lose

most of their features when matching, although they provide a high detection average.

References

[1] A. Madbouly, M. Wafy, and M.Mostafa,"Performance Assessment of Feature Detector-

Descriptor Combination". International Journal of Computer Science Issues, Vol. 12, Issue 5, 2015.

[2] . Ik, and K. zkan,"A Comparative Evaluation of Well-known Feature Detectors and Descriptors".

International Journal of Applied Mathematics, Electronics and Computers, ISSN:2147-8228, 2014.

[3] Tejas S Patel,"A Study on Feature Extraction Techniques for Image Mosaicing System".

IJARIIE-ISSN(O)-2395-4396, Vol. 2, Issue 3, 2016.

[4] S. Shaikh, and B. Patankar,"Multiple Feature Extraction Techniques in Image Stitching".

International Journal of Computer Applications (0975-8887) Vol. 123, No.15, 2015.

[5] ] Akash Patel, D. R. Kasat, S. Jain, and V.M. Thakare,". Performance Analysis of Various

Feature Detector and Descriptor for Real Time Video based Face Tracking". International Journal of

Computer Applications (0975- 8887), Vol. 93, No 1, 2014.

[6] E. Salahat, and M. Qasaimeh. "Recent Advances in Features Extraction and Description

Algorithms: A Comprehensive Survey". 2017 IEEE International Conference on Industrial

Technology (ICIT). pp.1059-1063, 2017.

[7] R. Menaka."A Methodical Review on Image Stitching and Video Stitching Techniques".

International Journal of Applied Engineering Research ISSN 0973-4562 Vol. 11, pp 3442- 3448, No.

5, 2016.

[8] S. Veni,” Image Processing Edge Detection Improvements And Its Applications”, International

Journal Of Innovations In Scientific And Engineering Research (IJISER), Vol 3 Issue 6 Jun 2016,

Pp.51-54.

[9] N. Kaushik, R. Rawat, and A. Bhalla."A Brief Study of Di_erent Feature Detector and

Descriptor". International Journal of Advanced Research in Computer and Communication

Engineering Vol. 5, Issue 4, 2016.

[10] Ch. Mohanbhai, and H. Bhaidasna."A Survey On Image Mosaicing Using Feature Based

Approach". International Journal of Engineering Development and Research, Vol. 5, Issue 1, ISSN:

2321-9939, 2017.


159

[11] E. Adel, M. Elmogy, and H. Elbakry."Image Stitching based on Feature Extraction Techniques:

A Survey". International Journal of Computer Applications (0975-8887) Vol. 99, No. 6, 2014.

[12] M. Hassaballah, A. Abdelmgeid, and H. Alshazly."Image Features Detection, Description and

Matching". Springer International Publishing Switzerland, 2016.

[13] E. Rublee, V. Rabaud, K, Konolige, and G.Bradski."ORB: an e_cient alternative to SIFT or

SURF". Proc of IEEE. International Conference on Computer Vision, pp. 2564-2571, 2011.

[14] K. Mikolajczyk, C. Schmid."Scale & a_ne invariant interest point detectors". International

Journal of Computer Vision. 60(1), pp.63-86, 2004.

[15] Pablo F. Alcantarilla, Adrien Bartoli, and Andrew J. Davison, "KAZE Features", University

dAuvergne, Clermont Ferrand, France, 2012.

[16] Andrew Ziegler, E. Christiansen,"Locally uniform comparison image descriptor". Neural

Information Processing Systems (NIPS): pp. 19, 2012.

[17] G. Levi and T. Hassner, LATCH: Learned Arrangements of Three Patch Codes, arXiv preprint

arXiv:1501.03719, 2015.

[18] A. Schmidt, M. Kraft, M. Fularz, and Z. Domagaa."Comparative Assessment of Point Feature

Detectors and Descriptors in the Context of Robot Navigation". Journal of Automation, Mobile

Robotics & Intelligent Systems. Vol. 7, 2013.

[19] Sh. Mistry, and A.Patel."Image Stitching using Harris Feature Detection". International

Research Journal of Engineering and Technology (IRJET). Vol. 03 Issue. 04, 2016.

[20] M. Calonder, V. Lepetit, C. Strecha, and P.Fua,"BRIEF: Binary Robust Independent Elementary

Features", in Proceedings of ECCV 2010, pp. 778-792. 2010.


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