scholarly journals Image Matching Process on Images Taken by a Cellular Phone

2003 ◽  
Vol 57 (9) ◽  
pp. 1177-1186 ◽  
Author(s):  
Yi Hu ◽  
Tomoharu Nagao ◽  
Masanori Okazaki ◽  
Taishi Chinone
Keyword(s):  
Image Matching ◽  
Cellular Phone ◽  
Matching Process

Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB

2021 ◽  
Vol 5 (4) ◽  
pp. 783-793
Author(s):  
Muhammad Muttabi Hudaya ◽  
Siti Saadah ◽  
Hendy Irawan
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Image Feature ◽  
Brute Force ◽  
K Nearest Neighbor ◽  
Matching Method ◽  
Average Precision ◽  
Detection Model ◽  
Matching Process

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.

scholarly journals OPTIMIZATION ALGORITHM OF TILTED IMAGE MATCHING BASED ON ADAPTIVE INITIAL OBJECT ASPECT

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 547-554
Author(s):  
C. Zhang ◽  
Y. Ge ◽  
Q. Zhang ◽  
B. Guo
Keyword(s):  
Least Squares ◽  
Image Matching ◽  
Normal Vector ◽  
Matching Method ◽  
Initial Value ◽  
Initial State ◽  
Matching Process ◽  
Low Degree ◽  
Verification Process ◽  
Initial Object

Abstract. When adopting the matching method of the least squares image based on object-patch to match tilted images, problems like the low degree of connection points for images with the discontinuity of depth or the discrepancy in elevation or low availability of aerotriangulation points would frequently appear. To address such problems, a tilted-image-matching algorithm based on an adaptive initial object-patch is proposed by this paper. By means of the existing initial values of the interior and exterior orientation elements of the tilted image and the information of object points generated in the matching process, the algorithm takes advantage of the method of multi-patch forward intersection and object variance partition so as to adaptively calculate the elevation of the object-patch and the initial value of the normal vector direction angle. Furthermore, this algorithm aims to solve the problem of difficulties in matching the tilted image with its corresponding points brought about by the low accuracy of the initial value of the tilted image when adopting the matching method of the least squares image based on object-patch to match the tilted image with high discrepancy in elevation. We adopt the algorithm as proposed in this paper and the least squares image matching method in which the initial state of the object-patch is horizontal to the object-patch respectively to conduct the verification process of comparing and matching two groups of tilted images. Finally, the effectiveness of the algorithm as proposed in this paper is verified by the testing results.

scholarly journals ROLE OF TIE-POINTS DISTRIBUTION IN AERIAL PHOTOGRAPHY

2016 ◽  
Vol XL-3/W4 ◽  
pp. 41-44
Author(s):  
S. Kerner ◽  
I. Kaufman ◽  
Y. Raizman
Keyword(s):  
Feature Extraction ◽  
Spatial Distribution ◽  
Aerial Photography ◽  
Image Matching ◽  
Feature Matching ◽  
Real Data ◽  
Matching Process ◽  
Feature Based ◽  
Extraction Stage

Automatic image matching algorithms, and especially feature-based methods, profoundly changed our understanding and requirements of tie points. The number of tie points has increased by orders of magnitude, yet the notions of accuracy and reliability of tie points remain equally important. The spatial distribution of tie points is less predictable, and is subject only to limited control. Feature-based methods also highlighted a conceptual division of the matching process into two separate stages – feature extraction and feature matching. <br><br> In this paper we discuss whether spatial distribution requirements, such as Von Gruber positions, are still relevant to modern matching methods. We argue that forcing such patterns might no longer be required in the feature extraction stage. However, we claim spatial distribution is important in the feature matching stage. <br><br> We will focus on terrains that are notorious for difficult matching, such as water bodies, with real data obtained by users of VisionMap’s A3 Edge camera and LightSpeed photogrammetric suite.

scholarly journals AN IMPROVED IMAGE MATCHING METHOD BASED ON SURF ALGORITHM

2018 ◽  
Vol XLII-3 ◽  
pp. 179-184 ◽  
Author(s):  
S. J. Chen ◽  
S. Z. Zheng ◽  
Z. G. Xu ◽  
C. C. Guo ◽  
X. L. Ma
Keyword(s):  
Remote Sensing ◽  
Information Entropy ◽  
Image Matching ◽  
Feature Matching ◽  
Operating Efficiency ◽  
Similarity Function ◽  
Entropy Theory ◽  
Matching Method ◽  
Matching Process ◽  
Invariant Transformation

Many state-of-the-art image matching methods, based on the feature matching, have been widely studied in the remote sensing field. These methods of feature matching which get highly operating efficiency, have a disadvantage of low accuracy and robustness. This paper proposes an improved image matching method which based on the SURF algorithm. The proposed method introduces color invariant transformation, information entropy theory and a series of constraint conditions to increase feature points detection and matching accuracy. First, the model of color invariant transformation is introduced for two matching images aiming at obtaining more color information during the matching process and information entropy theory is used to obtain the most information of two matching images. Then SURF algorithm is applied to detect and describe points from the images. Finally, constraint conditions which including Delaunay triangulation construction, similarity function and projective invariant are employed to eliminate the mismatches so as to improve matching precision. The proposed method has been validated on the remote sensing images and the result benefits from its high precision and robustness.

The Self-Image/Store Image Matching Process: An Empirical Test

1977 ◽  
Vol 50 (1) ◽  
pp. 63 ◽  
Author(s):  
Bruce L. Stern ◽  
Ronald F. Bush ◽  
Joseph F. Hair, Jr.
Keyword(s):  
Image Matching ◽  
Empirical Test ◽  
The Self ◽  
Store Image ◽  
Matching Process ◽  
Self Image

Differential Effect of Object Complexity on 2-D and 3-D Matching Processes

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 290-290
Author(s):  
S Nishina ◽  
T Inui
Keyword(s):  
Image Matching ◽  
Differential Effect ◽  
Processing Time ◽  
Low Complexity ◽  
Learning Ability ◽  
High Complexity ◽  
3 Dimensional ◽  
Matching Process ◽  
Recognition Experiment

Previously, we found that two matching processes work in parallel when an object is recognised from unknown viewpoints: the 2-dimensional (2-D) and the 3-dimensional (3-D) matching process. These processes were shown to differ in several respects, including recognition speed, generalisation range, and learning ability. We have now examined the effect of the complexity of an object on these two matching processes. We performed a recognition experiment where the subjects had to compare two sequentially presented images. The stimuli were objects that had different numbers of segments, presented for either 1.5 s (short condition) or 3.0 s (long condition). The different presentation times enabled us to separate the two processes, as 3-D matching requires a longer processing time. We adopted the ability to generalise from a known view as a measure of the performance of each process. Under the ‘short’ condition, the generalisation range for objects of high complexity was almost the same as that for objects of low complexity. Under the ‘long’ condition, however, the ranges for objects differing in complexity were significantly different. Our interpretation is that the effect of complexity was mainly associated with the 3-D matching process. The matching performed by the 2-D process under a shorter duration may be a simple image-to-image matching without recourse to the 3-D structure of the object.

scholarly journals DEVELOPMENT OF NEUROMORPHIC SIFT OPERATOR WITH APPLICATION TO HIGH SPEED IMAGE MATCHING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 681-688
Author(s):  
M. Shankayi ◽  
M. Saadatseresht ◽  
M. A. V. Bitetto
Keyword(s):  
Power Consumption ◽  
Image Matching ◽  
High Speed ◽  
Feature Detection ◽  
Image Sequence ◽  
Sift Algorithm ◽  
General Design ◽  
Matching Process ◽  
Mapping Process ◽  
Speed Accuracy

There was always a speed/accuracy challenge in photogrammetric mapping process, including feature detection and matching. Most of the researches have improved algorithm's speed with simplifications or software modifications which increase the accuracy of the image matching process. This research tries to improve speed without enhancing the accuracy of the same algorithm using Neuromorphic techniques. In this research we have developed a general design of a Neuromorphic ASIC to handle algorithms such as SIFT. We also have investigated neural assignment in each step of the SIFT algorithm. With a rough estimation based on delay of the used elements including MAC and comparator, we have estimated the resulting chip's performance for 3 scenarios, Full HD movie (Videogrammetry), 24 MP (UAV photogrammetry), and 88 MP image sequence. Our estimations led to approximate 3000 fps for Full HD movie, 250 fps for 24 MP image sequence and 68 fps for 88MP Ultracam image sequence which can be a huge improvement for current photogrammetric processing systems. We also estimated the power consumption of less than10 watts which is not comparable to current workflows.

scholarly journals Semi-Global Matching in Object Space

2015 ◽  
Vol XL-3/W2 ◽  
pp. 23-30 ◽  
Author(s):  
F. Bethmann ◽  
T. Luhmann
Keyword(s):  
Image Matching ◽  
New Method ◽  
Stereo Image ◽  
Aerial Image ◽  
Cost Calculation ◽  
Driver Assistance Systems ◽  
Object Space ◽  
Matching Process ◽  
3D Data ◽  
Global Matching

Semi-Global Matching (SGM) is a widespread algorithm for image matching which is used for very different applications, ranging from real-time applications (e.g. for generating 3D data for driver assistance systems) to aerial image matching. Originally developed for stereo-image matching, several extensions have been proposed to use more than two images within the matching process (multi-baseline matching, multi-view stereo). These extensions still perform the image matching in (rectified) stereo images and combine the pairwise results afterwards to create the final solution. This paper proposes an alternative approach which is suitable for the introduction of an arbitrary number of images into the matching process and utilizes image matching by using non-rectified images. The new method differs from the original SGM method mainly in two aspects: Firstly, the cost calculation is formulated in object space within a dense voxel raster by using the grey (or colour) values of all images instead of pairwise cost calculation in image space. Secondly, the semi-global (path-wise) minimization process is transferred into object space as well, so that the result of semi-global optimization leads to index maps (instead of disparity maps) which directly indicate the 3D positions of the best matches. Altogether, this yields to an essential simplification of the matching process compared to multi-view stereo (MVS) approaches. After a description of the new method, results achieved from two different datasets (close-range and aerial) are presented and discussed.

scholarly journals Image Matching via Loopy RNN

2017 ◽  
Author(s):  
Donghao Luo ◽  
Bingbing Ni ◽  
Yichao Yan ◽  
Xiaokang Yang
Keyword(s):  
Image Matching ◽  
Short Term Memory ◽  
Vision System ◽  
Feature Representation ◽  
Image Feature ◽  
Matching Process ◽  
Head Node ◽  
Long Short Term Memory ◽  
Input Gate ◽  
Matching Score

Most existing matching algorithms are one-off algorithms, i.e., they usually measure the distance between the two image feature representation vectors for only one time. In contrast, human's vision system achieves this task, i.e., image matching, by recursively looking at specific/related parts of both images and then making the final judgement. Towards this end, we propose a novel loopy recurrent neural network (Loopy RNN), which is capable of aggregating relationship information of two input images in a progressive/iterative manner and outputting the consolidated matching score in the final iteration. A Loopy RNN features two uniqueness. First, built on conventional long short-term memory (LSTM) nodes, it links the output gate of the tail node to the input gate of the head node, thus it brings up symmetry property required for matching. Second, a monotonous loss designed for the proposed network guarantees increasing confidence during the recursive matching process. Extensive experiments on several image matching benchmarks demonstrate the great potential of the proposed method.

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