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

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.

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1994
Author(s):  
Qian Ma ◽  
Wenting Han ◽  
Shenjin Huang ◽  
Shide Dong ◽  
Guang Li ◽  
...  

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.


2021 ◽  
Vol 5 (4) ◽  
pp. 783-793
Author(s):  
Muhammad Muttabi Hudaya ◽  
Siti Saadah ◽  
Hendy Irawan

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.


1976 ◽  
Vol 43 (3_suppl) ◽  
pp. 1299-1302
Author(s):  
Virginia Brabender ◽  
Christopher Clay

The present experiment tested the hypothesis that nominal processing increases as stimulus complexity increases. Subjects indicated whether two 4- or 12-sided forms, separated by an interval of .5 or 4.0 sec., were the same or different. “Same” responses corresponded to matches for physical or nominal identity. Longer RTs for high complexity than low complexity forms suggest that complexity affects the efficiency of visual processing rather than the occurrence of nominal processing. An interaction between type of match and interval, due to the longer RTs for matches of nominally identical forms at only the .5-sec. interval, indicates that at this interval, matches for physical and nominal identity are made with visual and nominal representations respectively.


Author(s):  
Yi Hu ◽  
Tomoharu Nagao ◽  
Masanori Okazaki ◽  
Taishi Chinone

Author(s):  
C. Zhang ◽  
Y. Ge ◽  
Q. Zhang ◽  
B. Guo

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.


2011 ◽  
pp. 295-316
Author(s):  
Markus Kampmann ◽  
Liang Zhang

This chapter introduces a complete framework for automatic adaptation of a 3D face model to a human face for visual communication applications like video conferencing or video telephony. First, facial features in a facial image are estimated. Then, the 3D face model is adapted using the estimated facial features. This framework is scalable with respect to complexity. Two complexity modes, a low complexity and a high complexity mode, are introduced. For the low complexity mode, only eye and mouth features are estimated and the low complexity face model Candide is adapted. For the high complexity mode, a more detailed face model is adapted, using eye and mouth features, eyebrow and nose features, and chin and cheek contours. Experimental results with natural videophone sequences show that with this framework automatic 3D face model adaptation with high accuracy is possible.


2019 ◽  
Vol 17 (02) ◽  
pp. 293-322 ◽  
Author(s):  
Cheng Wang ◽  
Ting Hu

Minimum error entropy (MEE) criterion is an important optimization method in information theoretic learning (ITL) and has been widely used and studied in various practical scenarios. In this paper, we shall introduce the online MEE algorithm for dealing with big datasets, associated with reproducing kernel Hilbert spaces (RKHS) and unbounded sampling processes. Explicit convergence rate will be given under the conditions of regularity of the regression function and polynomially decaying step sizes. Besides its low complexity, we will also show that the learning ability of online MEE is superior to the previous work in the literature. Our main techniques depend on integral operators on RKHS and probability inequalities for random variables with values in a Hilbert space.


1971 ◽  
Vol 1 (2) ◽  
pp. 99-112 ◽  
Author(s):  
J. K. Jeglum ◽  
C. F. Wehrhahn ◽  
J. M. A. Swan

Data from a survey of lowland, mainly peatland, vegetation were subjected to environmental ordination based on measurements of water level and water conductivity, and to vegetational ordination derived from principal component analysis (P.C.A.). Analyzed were the total set of the data ("all types"), half sets ("nonwoody" and "woody types") and quarter sets (stands of "marshes", "meadows", "shrub fens", and "other woody types"); the number of distinct physiognomic groups in a set of data, and presumably the amount of contained heterogeneity, decreased at each segmentation.The effectiveness of the ordination models was tested by correlating measured distances in two-dimensional ordination models with 2W/(A + B) indices of vegetational similarity for randomly selected pairs of types or stands. As the physiognomic complexity decreased, the effectiveness of the P.C.A. vegetational ordination increased whereas that of the environmental ordination decreased. The environmental ordination seemed most appropriate to the data encompassing high complexity (total data set), while the P.C.A. vegetational ordination seemed most appropriate to data with low complexity (quarter sets of the data).


Nanomaterials ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 892
Author(s):  
Dieter Reenaers ◽  
Wouter Marchal ◽  
Ianto Biesmans ◽  
Philippe Nivelle ◽  
Jan D’Haen ◽  
...  

The field of printed electronics is rapidly evolving, producing low cost applications with enhanced performances with transparent, stretchable properties and higher reliability. Due to the versatility of printed electronics, industry can consider the implementation of electronics in a way which was never possible before. However, a post-processing step to achieve conductive structures—known as sintering—limits the production ease and speed of printed electronics. This study addresses the issues related to fast sintering without scarifying important properties such as conductivity and surface roughness. A drop-on-demand inkjet printer is employed to deposit silver nanoparticle-based inks. The post-processing time of these inks is reduced by replacing the conventional oven sintering procedure with the state-of-the-art method, named near-infrared sintering. By doing so, the post-processing time shortens from 30–60 min to 6–8 s. Furthermore, the maximum substrate temperature during sintering is reduced from 200 °C to 120 °C. Based on the results of this study, one can conclude that near-infrared sintering is a ready-to-industrialize post-processing method for the production of printed electronics, capable of sintering inks at high speed, low temperature and with low complexity. Furthermore, it becomes clear that ink optimization plays an important role in processing inkjet printable inks, especially after being near-infrared sintered.


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