Image-matching framework based on region partitioning for target image location

2020 ◽  
Vol 74 (3) ◽  
pp. 269-286
Author(s):  
Xiaomin Liu ◽  
Jun-Bao Li ◽  
Jeng-Shyang Pan ◽  
Shuo Wang ◽  
Xudong Lv ◽  
...  
Keyword(s):  
2021 ◽  
pp. 174702182110097
Author(s):  
Niamh Hunnisett ◽  
Simone Favelle

Unfamiliar face identification is concerningly error prone, especially across changes in viewing conditions. Within-person variability has been shown to improve matching performance for unfamiliar faces, but this has only been demonstrated using images of a front view. In this study, we test whether the advantage of within-person variability from front views extends to matching to target images of a face rotated in view. Participants completed either a simultaneous matching task (Experiment 1) or a sequential matching task (Experiment 2) in which they were tested on their ability to match the identity of a face shown in an array of either one or three ambient front-view images, with a target image shown in front, three-quarter, or profile view. While the effect was stronger in Experiment 2, we found a consistent pattern in match trials across both experiments in that there was a multiple image matching benefit for front, three-quarter, and profile-view targets. We found multiple image effects for match trials only, indicating that providing observers with multiple ambient images confers an advantage for recognising different images of the same identity but not for discriminating between images of different identities. Signal detection measures also indicate a multiple image advantage despite a more liberal response bias for multiple image trials. Our results show that within-person variability information for unfamiliar faces can be generalised across views and can provide insights into the initial processes involved in the representation of familiar faces.


2012 ◽  
Vol 220-223 ◽  
pp. 2799-2802
Author(s):  
Tao Jia

The intelligent monitoring refers to the use of pattern recognition technology, digital signal processing, artificial intelligence theory and automatic control technology, and integrating video surveillance into the monitoring system, to enable the system become intelligent. The core technology for the system is the identification and matching of moving targets; and the fast & exact match detection of a moving target is the key step and technical basis for subsequent identification. Image-based matching has a wide range of applications in the field of industrial product testing, identification of aerial targets, the identification of ground targets. In this paper, I’d discuss over the program of utilizing 1K50 + LPC2106 processor to achieve the target detection, together with the specific algorithm. Experimental results show that the processing system is absolutely capable of real-time detection and matching of the target image.


2009 ◽  
Vol 21 (1) ◽  
pp. 121-127
Author(s):  
Sota Shimizu ◽  
◽  
Joel W. Burdick ◽  

This paper aims at the acquisition of a robust feature for rotation, scale, and translation-invariant image matching of space-variant images from a fovea sensor. The proposed model eccentricity compensator corrects deformation in log-polar images when the fovea sensor is not centered on the target image, that is, when eccentricity exists. An image simulator in a discrete space implements this model through its geometrical formulation. This paper also proposes Unreliable Feature Omission (UFO) using the Discrete Wavelet Transform. UFO reduces local high frequency noise appearing in the space-variant image when the eccentricity changes. It discards coefficients when they are regarded as unreliable, based on digitized errors in the input image from the fovea sensor. The first simulation estimates the compensator by comparing it with other polar images. This result shows the compensator performs well, and its root mean square error (RMSE) changes only by up to 2.54% on the condition that the eccentricity is within 34.08°. The second simulation shows UFO performs well for the log-polar image remapped by the eccentricity compensator when white Gaussian noise (WGN) is added. The result from the Daubechies (7, 9) biorthogonal wavelet shows UFO reduces the RMSE by up to 0.40 %, even if the WGN is not added, when the eccentricity is within 34.08°. This paper is the full translation from the transactions of JSME Vol.73, No.733.


2020 ◽  
Vol 12 (3) ◽  
pp. 465 ◽  
Author(s):  
Jae-Hyun Park ◽  
Woo-Jeoung Nam ◽  
Seong-Whan Lee

In this paper, we propose a novel method to precisely match two aerial images that were obtained in different environments via a two-stream deep network. By internally augmenting the target image, the network considers the two-stream with the three input images and reflects the additional augmented pair in the training. As a result, the training process of the deep network is regularized and the network becomes robust for the variance of aerial images. Furthermore, we introduce an ensemble method that is based on the bidirectional network, which is motivated by the isomorphic nature of the geometric transformation. We obtain two global transformation parameters without any additional network or parameters, which alleviate asymmetric matching results and enable significant improvement in performance by fusing two outcomes. For the experiment, we adopt aerial images from Google Earth and the International Society for Photogrammetry and Remote Sensing (ISPRS). To quantitatively assess our result, we apply the probability of correct keypoints (PCK) metric, which measures the degree of matching. The qualitative and quantitative results show the sizable gap of performance compared to the conventional methods for matching the aerial images. All code and our trained model, as well as the dataset are available online.


2014 ◽  
Vol 51 (9) ◽  
pp. 091002
Author(s):  
Zhang Lijuan ◽  
Yang Jinhua Jiang Yutong ◽  
Li Dongming ◽  
Tan Fang

2012 ◽  
Vol 239-240 ◽  
pp. 1152-1157
Author(s):  
Lu Yun Zhang ◽  
Qiang Wang ◽  
Hai Yan Liu ◽  
Gang Wang

A new algorithm is presented in this paper to determinate the point correspondences on contour between template image and its target after affine transformation. In the algorithm, the singular value decomposition(SVD)is applied to the contour point sets of the template and target image respectively for eliminating the influences of the shear and scale in the affine transformation. The Euclidean distance between the contour point and the center of the shape are taken as the feature to form the reference sequence and comparative sequences, and then grey relational analysis (GRA) is used to find the best correlation sequence. After two contour sequences with the best correlation are found, the corresponding points between the two contours can be decided also. Finally the affine transformation parameter can be calculated and image matching can be realized by this way. Compared with the similar methods, experiments show that the proposed method has lower computational complexity and better accurate for image matching.


Author(s):  
Jianfei Yu ◽  
Jing Jiang

As an important task in Sentiment Analysis, Target-oriented Sentiment Classification (TSC) aims to identify sentiment polarities over each opinion target in a sentence. However, existing approaches to this task primarily rely on the textual content, but ignoring the other increasingly popular multimodal data sources (e.g., images), which can enhance the robustness of these text-based models. Motivated by this observation and inspired by the recently proposed BERT architecture, we study Target-oriented Multimodal Sentiment Classification (TMSC) and propose a multimodal BERT architecture. To model intra-modality dynamics, we first apply BERT to obtain target-sensitive textual representations. We then borrow the idea from self-attention and design a target attention mechanism to perform target-image matching to derive target-sensitive visual representations. To model inter-modality dynamics, we further propose to stack a set of self-attention layers to capture multimodal interactions. Experimental results show that our model can outperform several highly competitive approaches for TSC and TMSC.


2012 ◽  
Vol 505 ◽  
pp. 352-356
Author(s):  
Jian Xiong Wang ◽  
Yu Lan Wang ◽  
Bin Hong ◽  
Hong Ling Zhang

According to the traditional gray-scale value matching algorithm and its defect of high time complexity, this paper presents a matching algorithm based background subtraction. First we find moving target in the background by subtraction, and then match the template image to the target image of motion, which greatly reduces the amount of calculation and improves the matching speed. We effectively predict the target’s location in the next frame by five-point linear prediction method. Simulation shows that the algorithm is better than the traditional gray-scale matching algorithm and it has strong robustness.


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