Automated scene matching in movies by Schaffalitzky, F, Zisserman, A

“…<p>We describe progress in matching shots which are images of the same 3D scene in a film. …”

Automated location matching in movies by Schaffalitzky, F, Zisserman, A

“…We develop analogues of local spatial consistency, cross-correlation, and epipolar geometry for these tracks. Results of matching shots for a number of very different scene types are illustrated on two entire commercial films.…”

Wide baseline stereo matching by Pritchett, P, Zisserman, A

“…Two novel infrastructure developments are also described: the automatic generation of local homographies, and the combination of possibly conflicting sets of matches prior to RANSAC estimation. The wide baseline matching algorithm is demonstrated on a number of image pairs with varying relative motion, and for different scene types. …”

Automatic line matching across views by Schmid, C, Zisserman, A

“…For image pairs epipolar geometry facilitates the computation of a cross-correlation based matching score for putative line correspondences. For image triplets cross-correlation matching scores are used in conjunction with line transfer based on the trifocal geometry. …”

The geometry and matching of lines and curves over multiple views by Schmid, C, Zisserman, A

“…<br> Experimental results are given for image pairs and triplets, for varying motions between views, and for different scene types. The methods are applicable to line/curve matching in stereo and trinocular rigs, and as a starting point for line/curve matching through monocular image sequences.…”

Graph edit distance reward : learning to edit scene graph by Chen, Lichang, Lin, Guosheng, Wang, Shijie, Wu, Qingyao

“…To be specific, in order to learn editing scene graphs as the semantics given by texts, we propose a Graph Edit Distance Reward, which is based on the Policy Gradient and Graph Matching algorithm, to optimize neural symbolic model. …”
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Video Google: a text retrieval approach to object matching in videos by Sivic, J, Zisserman, A

“…We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. …”

RobustMat: neural diffusion for street landmark patch matching under challenging environments by She, Rui, Kang, Qiyu, Wang, Sijie, Yang, Yuan-Rui, Zhao, Kai, Song, Yang, Tay, Wee Peng

“…In various computer perception tasks for AVs, it may be helpful to match landmark patches taken by an onboard camera with other landmark patches captured at a different time or saved in a street scene image database. …”
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Video anomaly search in crowded scenes via spatio-temporal motion context by Cong, Yang, Yuan, Junsong, Tang, Yandong

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Do we match ? Negotiating gender expectations and identities through mobile dating applications in Singapore by Tan, Si Ting Iris, Er, Kok Tong Marcus, Wee, Shi Min

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Multi-view matching for unordered image sets, or “how do I organize my holiday snaps?” by Schaffalitzky, F, Zisserman, A

“…Instead, we investiate how a combination of image invariants, covariants, and multiple view relations can be used in concord to enable efficient multiple view matching. The result is a matching algorithm which is linear in the number of views.…”

Prevalence of health-risk behaviors and mental well-being of ASEAN university students in COVID-19 pandemic by Rahman, Hanif Abdul, Amornsriwatanakul, Areekul, Abdul-Mumin, Khadizah H., Agustiningsih, Denny, Chaiyasong, Surasak, Chia, Michael, Chupradit, Supat, Huy, Le Quang, Ivanovitch, Katiya, Nurmala, Ira, Majid, Hazreen B. Abdul, Nazan, Ahmad Iqmer Nashriq Mohd, Rodjarkpai, Yuvadee, de la Cruz, Ma Henrietta Teresa O., Mahmudiono, Trias, Sriboonma, Krissachai, Sudnongbua, Supaporn, Vidiawati, Dhanasari, Wattanapisit, Apichai, Charoenwattana, Sukanya, Cahyani, Nani, Car, Josip, Ho, Ringo Moon-Ho, Rosenberg, Michael

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Color transfer between images by Liang, Ruyin

“…It changes the dominant color of a source image to match the color characteristics of the reference image, while the scene and the objects of the source image remain unchanged. …”
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Automated reconstruction from multiple photographs by Zisserman, A, Schaffalitzky, F, Werner, T, Fitzgibbon, A

“…The first is wide baseline matching - establishing correspondences and cameras for images acquired from very different viewpoints. …”

Visual place recognition for autonomous robots using deep learning by Huang, Yifeng

“…Visual place recognition has become a challenging and attractive field in computer vision and robotics because it involves many methods to recognize the appearance of natural scenes that may be different. In autonomous and unmanned aerial vehicles, visual location recognition helps to detect actual destinations and locations In order to match standard features from different angles of a scene, we propose a global feature matching method, which is separate from the current popular local feature matching method. …”
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Sketch animation based on sketch-annotated key-frame in normal video by Lee, Tian Hui.

“…In this project, a software program is proposed to investigate how sketches can be animated through motion transfer from a video of the actual scene to aid animators in making better and more realistic animation. …”
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Colour transfer between images by Ng, Remus Jing Heng

“…Uses of this technique include but are not limited to, automatic colouring a grayscale image, altering of the mood of a particular scene, or colour matching an image to fit in a set. …”
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Object recognition and pose estimation in robotic grasping system by Zhou, Jiadong

“…In this report, we present a perception system which combines deep learning and 3D shape matching techniques to overcome those difficulties. …”
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Evolving code with a large language model by Hemberg, Erik, Moskal, Stephen, O’Reilly, Una-May

“…Algorithms that use Large Language Models (LLMs) to evolve code arrived on the Genetic Programming (GP) scene very recently. We present LLM_GP, a general LLM-based evolutionary algorithm designed to evolve code. …”
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Negative obstacle detection using stereo vision by You, Zhiyong

“…Our brain would then compute the depth of the objects with the disparity, eventually, 3D scenes are constructed. Computer Binocular Vision make use of this theory, use two cameras to capture images from same scene, obtained the images with disparity. …”
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