Method and apparatus for predicting the direction of movement in machine vision

A computer-simulated cortical network is presented. The network is capable of computing the visibility of shifts in the direction of movement. Additionally, the network can compute the following: (1) the magnitude of the position difference between the test and background patterns; (2) localized contrast differences at different spatial scales analyzed by computing temporal gradients of the difference and sum of the outputs of paired even- and odd-symmetric bandpass filters convolved with the input pattern; and (3) the direction of a test pattern moved relative to a textured background. The direction of movement of an object in the field of view of a robotic vision system is detected in accordance with nonlinear Gabor function algorithms. The movement of objects relative to their background is used to infer the 3-dimensional structure and motion of object surfaces

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation

Novel Approaches in Structured Light Illumination

Among the various approaches to 3-D imaging, structured light illumination (SLI) is widely spread. SLI employs a pair of digital projector and digital camera such that the correspondences can be found based upon the projecting and capturing of a group of designed light patterns. As an active sensing method, SLI is known for its robustness and high accuracy. In this dissertation, I study the phase shifting method (PSM), which is one of the most employed strategy in SLI. And, three novel approaches in PSM have been proposed in this dissertation. First, by regarding the design of patterns as placing points in an N-dimensional space, I take the phase measuring profilometry (PMP) as an example and propose the edge-pattern strategy which achieves maximum signal to noise ratio (SNR) for the projected patterns. Second, I develop a novel period information embedded pattern strategy for fast, reliable 3-D data acquisition and reconstruction. The proposed period coded phase shifting strategy removes the depth ambiguity associated with traditional phase shifting patterns without reducing phase accuracy or increasing the number of projected patterns. Thus, it can be employed for high accuracy realtime 3-D system. Then, I propose a hybrid approach for high quality 3-D reconstructions with only a small number of illumination patterns by maximizing the use of correspondence information from the phase, texture, and modulation data derived from multi-view, PMP-based, SLI images, without rigorously synchronizing the cameras and projectors and calibrating the device gammas. Experimental results demonstrate the advantages of the proposed novel strategies for 3-D SLI systems

The seventh visual object tracking VOT2019 challenge results

180The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOTST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on 'real-time' shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website.openopenKristan M.; Matas J.; Leonardis A.; Felsberg M.; Pflugfelder R.; Kamarainen J.-K.; Zajc L.C.; Drbohlav O.; Lukezic A.; Berg A.; Eldesokey A.; Kapyla J.; Fernandez G.; Gonzalez-Garcia A.; Memarmoghadam A.; Lu A.; He A.; Varfolomieiev A.; Chan A.; Tripathi A.S.; Smeulders A.; Pedasingu B.S.; Chen B.X.; Zhang B.; Baoyuanwu B.; Li B.; He B.; Yan B.; Bai B.; Li B.; Li B.; Kim B.H.; Ma C.; Fang C.; Qian C.; Chen C.; Li C.; Zhang C.; Tsai C.-Y.; Luo C.; Micheloni C.; Zhang C.; Tao D.; Gupta D.; Song D.; Wang D.; Gavves E.; Yi E.; Khan F.S.; Zhang F.; Wang F.; Zhao F.; De Ath G.; Bhat G.; Chen G.; Wang G.; Li G.; Cevikalp H.; Du H.; Zhao H.; Saribas H.; Jung H.M.; Bai H.; Yu H.; Peng H.; Lu H.; Li H.; Li J.; Li J.; Fu J.; Chen J.; Gao J.; Zhao J.; Tang J.; Li J.; Wu J.; Liu J.; Wang J.; Qi J.; Zhang J.; Tsotsos J.K.; Lee J.H.; Van De Weijer J.; Kittler J.; Ha Lee J.; Zhuang J.; Zhang K.; Wang K.; Dai K.; Chen L.; Liu L.; Guo L.; Zhang L.; Wang L.; Wang L.; Zhang L.; Wang L.; Zhou L.; Zheng L.; Rout L.; Van Gool L.; Bertinetto L.; Danelljan M.; Dunnhofer M.; Ni M.; Kim M.Y.; Tang M.; Yang M.-H.; Paluru N.; Martinel N.; Xu P.; Zhang P.; Zheng P.; Zhang P.; Torr P.H.S.; Wang Q.Z.Q.; Guo Q.; Timofte R.; Gorthi R.K.; Everson R.; Han R.; Zhang R.; You S.; Zhao S.-C.; Zhao S.; Li S.; Li S.; Ge S.; Bai S.; Guan S.; Xing T.; Xu T.; Yang T.; Zhang T.; Vojir T.; Feng W.; Hu W.; Wang W.; Tang W.; Zeng W.; Liu W.; Chen X.; Qiu X.; Bai X.; Wu X.-J.; Yang X.; Chen X.; Li X.; Sun X.; Chen X.; Tian X.; Tang X.; Zhu X.-F.; Huang Y.; Chen Y.; Lian Y.; Gu Y.; Liu Y.; Chen Y.; Zhang Y.; Xu Y.; Wang Y.; Li Y.; Zhou Y.; Dong Y.; Xu Y.; Zhang Y.; Li Y.; Luo Z.W.Z.; Zhang Z.; Feng Z.-H.; He Z.; Song Z.; Chen Z.; Zhang Z.; Wu Z.; Xiong Z.; Huang Z.; Teng Z.; Ni Z.Kristan, M.; Matas, J.; Leonardis, A.; Felsberg, M.; Pflugfelder, R.; Kamarainen, J. -K.; Zajc, L. C.; Drbohlav, O.; Lukezic, A.; Berg, A.; Eldesokey, A.; Kapyla, J.; Fernandez, G.; Gonzalez-Garcia, A.; Memarmoghadam, A.; Lu, A.; He, A.; Varfolomieiev, A.; Chan, A.; Tripathi, A. S.; Smeulders, A.; Pedasingu, B. S.; Chen, B. X.; Zhang, B.; Baoyuanwu, B.; Li, B.; He, B.; Yan, B.; Bai, B.; Li, B.; Li, B.; Kim, B. H.; Ma, C.; Fang, C.; Qian, C.; Chen, C.; Li, C.; Zhang, C.; Tsai, C. -Y.; Luo, C.; Micheloni, C.; Zhang, C.; Tao, D.; Gupta, D.; Song, D.; Wang, D.; Gavves, E.; Yi, E.; Khan, F. S.; Zhang, F.; Wang, F.; Zhao, F.; De Ath, G.; Bhat, G.; Chen, G.; Wang, G.; Li, G.; Cevikalp, H.; Du, H.; Zhao, H.; Saribas, H.; Jung, H. M.; Bai, H.; Yu, H.; Peng, H.; Lu, H.; Li, H.; Li, J.; Li, J.; Fu, J.; Chen, J.; Gao, J.; Zhao, J.; Tang, J.; Li, J.; Wu, J.; Liu, J.; Wang, J.; Qi, J.; Zhang, J.; Tsotsos, J. K.; Lee, J. H.; Van De Weijer, J.; Kittler, J.; Ha Lee, J.; Zhuang, J.; Zhang, K.; Wang, K.; Dai, K.; Chen, L.; Liu, L.; Guo, L.; Zhang, L.; Wang, L.; Wang, L.; Zhang, L.; Wang, L.; Zhou, L.; Zheng, L.; Rout, L.; Van Gool, L.; Bertinetto, L.; Danelljan, M.; Dunnhofer, M.; Ni, M.; Kim, M. Y.; Tang, M.; Yang, M. -H.; Paluru, N.; Martinel, N.; Xu, P.; Zhang, P.; Zheng, P.; Zhang, P.; Torr, P. H. S.; Wang, Q. Z. Q.; Guo, Q.; Timofte, R.; Gorthi, R. K.; Everson, R.; Han, R.; Zhang, R.; You, S.; Zhao, S. -C.; Zhao, S.; Li, S.; Li, S.; Ge, S.; Bai, S.; Guan, S.; Xing, T.; Xu, T.; Yang, T.; Zhang, T.; Vojir, T.; Feng, W.; Hu, W.; Wang, W.; Tang, W.; Zeng, W.; Liu, W.; Chen, X.; Qiu, X.; Bai, X.; Wu, X. -J.; Yang, X.; Chen, X.; Li, X.; Sun, X.; Chen, X.; Tian, X.; Tang, X.; Zhu, X. -F.; Huang, Y.; Chen, Y.; Lian, Y.; Gu, Y.; Liu, Y.; Chen, Y.; Zhang, Y.; Xu, Y.; Wang, Y.; Li, Y.; Zhou, Y.; Dong, Y.; Xu, Y.; Zhang, Y.; Li, Y.; Luo, Z. W. Z.; Zhang, Z.; Feng, Z. -H.; He, Z.; Song, Z.; Chen, Z.; Zhang, Z.; Wu, Z.; Xiong, Z.; Huang, Z.; Teng, Z.; Ni, Z

LookOut! Interactive Camera Gimbal Controller for Filming Long Takes

The job of a camera operator is more challenging, and potentially dangerous, when filming long moving camera shots. Broadly, the operator must keep the actors in-frame while safely navigating around obstacles, and while fulfilling an artistic vision. We propose a unified hardware and software system that distributes some of the camera operator's burden, freeing them up to focus on safety and aesthetics during a take. Our real-time system provides a solo operator with end-to-end control, so they can balance on-set responsiveness to action vs planned storyboards and framing, while looking where they're going. By default, we film without a field monitor. Our LookOut system is built around a lightweight commodity camera gimbal mechanism, with heavy modifications to the controller, which would normally just provide active stabilization. Our control algorithm reacts to speech commands, video, and a pre-made script. Specifically, our automatic monitoring of the live video feed saves the operator from distractions. In pre-production, an artist uses our GUI to design a sequence of high-level camera "behaviors." Those can be specific, based on a storyboard, or looser objectives, such as "frame both actors." Then during filming, a machine-readable script, exported from the GUI, ties together with the sensor readings to drive the gimbal. To validate our algorithm, we compared tracking strategies, interfaces, and hardware protocols, and collected impressions from a) film-makers who used all aspects of our system, and b) film-makers who watched footage filmed using LookOut.Comment: V2: - Fixed typos. - Cleaner supplemental. - New plot in control section with same data from a supplemental vide

Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes

In the life cycle of highly automated systems operating in an open and dynamic environment, the ability to adjust to emerging challenges is crucial. For systems integrating data-driven AI-based components, rapid responses to deployment issues require fast access to related data for testing and reconfiguration. In the context of automated driving, this especially applies to road obstacles that were not included in the training data, commonly referred to as out-of-distribution (OoD) road obstacles. Given the availability of large uncurated recordings of driving scenes, a pragmatic approach is to query a database to retrieve similar scenarios featuring the same safety concerns due to OoD road obstacles. In this work, we extend beyond identifying OoD road obstacles in video streams and offer a comprehensive approach to extract sequences of OoD road obstacles using text queries, thereby proposing a way of curating a collection of OoD data for subsequent analysis. Our proposed method leverages the recent advances in OoD segmentation and multi-modal foundation models to identify and efficiently extract safety-relevant scenes from unlabeled videos. We present a first approach for the novel task of text-based OoD object retrieval, which addresses the question ''Have we ever encountered this before?''.Comment: 11 pages, 7 figures, and 3 table