Target Relocation Research Articles

Distraction from long-term memory in visual search: Acquired procedural and template-based memory interfere with contextual cueing after target re-location

ABSTRACT Participants can learn to faster detect targets embedded in repeatedly encountered spatial arrangements of distractors – termed the “contextual cueing” (CC) effect. However, cueing is severely compromised following target relocation in an otherwise unchanged distractor arrangement. Previous research demonstrated that this re-location cost is due to persistent misguidance of search towards the original location. Since CC reflects top-down guidance of contextual memory, this misguidance is an instance of a “distraction” effect that operates from acquired memory, rather than being driven by salient but irrelevant stimuli in the display. While traditional accounts of CC emphasize the acquisition of search-guiding memory “templates” specific to particular displays, contextual learning also tunes attentional (oculomotor) scanning routines to the overall statistics of the display arrangements, yielding a context-unspecific LT “proceduralization” of search. Using reaction-time and oculomotor-scanning measures, we confirmed both mechanisms to contribute to initial contextual learning as well as the “distraction” effect produced by relocating the targets of repeated displays. We suspect that guidance and misguidance of search by repeated contexts involve two complementary LT mechanisms: procedural optimization of broad, i.e., display-generic, scanning routines, and learning of where to expect the target in specific repeated-context displays.

Multipath Exploitation: Non-Line-of-Sight Target Relocation in Array-InSAR 3-D Imaging

Multipath Exploitation: Non-Line-of-Sight Target Relocation in Array-InSAR 3-D Imaging

Robot assisted Fetoscopic Laser Coagulation: Improvements in navigation, re-location and coagulation

Fetoscopic Laser Coagulation (FLC) for Twin to Twin Transfusion Syndrome is a challenging intervention due to the working conditions: low quality images acquired from a 3 mm fetoscope inside a turbid liquid environment, local view of the placental surface, unstable surgical field and delicate tissue layers. FLC is based on locating, coagulating and reviewing anastomoses over the placenta’s surface. The procedure demands the surgeons to generate a mental map of the placenta with the distribution of the anastomoses, maintaining, at the same time, precision in coagulation and protecting the placenta and amniotic sac from potential damages. This paper describes a teleoperated platform with a cognitive-based control that provides assistance to improve patient safety and surgery performance during fetoscope navigation, target re-location and coagulation processes. A comparative study between manual and teleoperated operation, executed in dry laboratory conditions, analyzes basic fetoscopic skills: fetoscope navigation and laser coagulation. Two exercises are proposed: first, fetoscope guidance and precise coagulation. Second, a resolved placenta (all anastomoses are indicated) to evaluate navigation, re-location and coagulation. The results are analyzed in terms of economy of movement, execution time, coagulation accuracy, amount of coagulated placental surface and risk of placenta puncture. In addition, new metrics, based on navigation and coagulation maps evaluate robotic performance. The results validate the developed platform, showing noticeable improvements in all the metrics.

Mission impossible? Spatial context relearning following a target relocation event depends on cue predictiveness

Visual search for a target is faster when the spatial layout of distractors is repeatedly encountered, illustrating that statistical learning of contextual invariances facilitates attentional guidance (contextual cueing; Chun & Jiang, 1998, Cognitive Psychology, 36, 28–71). While contextual learning is usually relatively efficient, relocating the target to an unexpected location (within an otherwise unchanged search layout) typically abolishes contextual cueing and the benefits deriving from invariant contexts recover only slowly with extensive training (Zellin et al., 2014, Psychonomic Bulletin & Review, 21(4), 1073–1079). However, a recent study by Peterson et al. (2022, Attention, Perception, & Psychophysics, 84(2), 474–489) in fact reported rather strong adaptation of spatial contextual memories following target position changes, thus contrasting with prior work. Peterson et al. argued that previous studies may have been underpowered to detect a reliable recovery of contextual cueing after the change. However, their experiments also used a specific display design that frequently presented the targets at the same locations, which might reduce the predictability of the contextual cues thereby facilitating its flexible relearning (irrespective of statistical power). The current study was a (high-powered) replication of Peterson et al., taking into account both statistical power and target overlap in context-memory adaptation. We found reliable contextual cueing for the initial target location irrespective of whether the targets shared their location across multiple displays, or not. However, contextual adaptation following a target relocation event occurred only when target locations were shared. This suggests that cue predictability modulates contextual adaptation, over and above a possible (yet negligible) influence of statistical power.

Re-detection and distractor association from a global perspective: A long-term tracking system

Great success has been achieved in short-term visual object tracking in recent years. However, long-term tracking which is closer to practical applications still lacks enough attention, especially for the target disappearances and reappearances which are common phenomena and important properties in long-term tracking due to full occlusion and out-of-view. In this paper, we propose a long-term tracking framework based on the cooperation of a short-term local tracker and the high-quality global re-detection and distractor association mechanisms. The former mechanism provides target candidates for target relocation, and the latter utilizes the Kalman Filter and Hungarian algorithm with motion cues to build distractor tracklets which attempts to avoid incorrect target relocation after the target disappears. We apply the tracking framework on the state-of-the-art algorithm STARK and evaluate the performance on the popular long-term benchmarks VOTLT-2020 and TLP. The experimental results show the effectiveness of the proposed framework and our tracker performs well compared with other competing algorithms.

The acquisition but not adaptation of contextual memories is enhanced in action video-game players

Visual search is facilitated when a target item is positioned within an invariant arrangement of task-irrelevant distractor elements (relative to non-repeated arrangements), because learnt target-distractor spatial associations guide visual search. While such configural search templates stored in long-term memory (LTM) cue focal attention towards the search-for target after only a few display repetitions, adaptation of existing configural LTM requires extensive training. The current work examined the important question whether individuals claimed to have better attention performance (i.e., action video game players; AVGP) show improved acquisition vs. adaptation of configural LTM (relative to no-gamers; NAVGP) in a visual-search task with repeated and non-repeated search configurations and consisting of an initial learning phase and, following target relocation, a subsequent adaptation phase. We found that contextual facilitation of search reaction times was more pronounced for AVGP relative to NAVGP in initial learning, probably reflecting enhanced learn-to-learn capabilities in the former individuals. However, this advantage did not carry over to the adaptation phase, in which gamers and non-gamers exhibited similar performance and suggesting that attention control required for overcoming visual distraction from previously learned (but no more relevant) target positions is relatively uninfluenced by action-game experience.

Target re-location kernel correlation filtered visual tracking with fused deep feature

Target re-location kernel correlation filtered visual tracking with fused deep feature

Spatial context target relearning following a target relocation event: Not mission impossible.

Our visual system relies on memory to store and retrieve goal-relevant structures and information from the environment for the purpose of optimizing the allocation of attention. This concept, referred to as contextual cueing, has been demonstrated using visual search tasks, wherein repeated visual contexts lead to reduced search times compared with random displays. Subsequently, when an unexpected change occurs in the environment, or memory fails, a cognitive expense is incurred as the mind tries to resolve the conflict with the memory of the previous environmental context. How memory resolves these conflicts and is updated is of great interest. Previous studies showed that, without extensive practice, individuals were unable to associate a secondary target location with a previously learned spatial context following the relocation of the initially learned target. Here, we explored variables that could potentially affect contextual learning and relearning, such as display size, crowding, context color, and whether the target switched to a previously occupied or unoccupied location. In a series of four experiments, we find relearning occurring in all instances. Previous research may have suffered from underpowered designs.

Knowledge-Aided Ground Moving Target Relocation for Airborne Dual-Channel Wide-Area Radar by Exploiting the Antenna Pattern Information

This paper addresses the problem of ground moving target relocation (GMTR) for airborne dual-channel wide-area radar systems. The monopulse technique can be utilized to perform GMTR. However, in real conditions, the GMTR performance degrades greatly due to the effect of channel mismatch. To tackle this problem, prior knowledge of the antenna pattern information is fully utilized to improve the GMTR performance, and a knowledge-aided GMTR algorithm (KA-GMTR) for airborne dual-channel wide-area radar is proposed in this paper. First, the GMTR model for the two receiving channels is analyzed. The channel mismatch model is constructed, and its expression is derived. Then, the channel mismatch phase error is well estimated by exploiting the prior antenna pattern information based on the least squares (LS) method. Meanwhile, the knowledge-aided monopulse curve (KA-MPC) is derived to perform the direction of arrival (DOA) estimation for potential targets. Finally, KA-GMTR, based on the KA-MPC, is performed to estimate the azimuth offsets and relocate the geometry positions of the potential targets when channel mismatch occurs. Moreover, the target relocation performance is analyzed, and the intrinsic reason that degrades the target relocation accuracy is figured out. The performance assessment based on airborne real-data, also in comparison to the conventional GMTR method, has demonstrated that our proposed KA-GMTR algorithm offers preferable target relocation results under channel mismatch scenarios.

Multi-Feature Fusion Target Re-Location Tracking Based on Correlation Filters

Target tracking has been a research hotspot in computer vision, and the correlation filtered target tracking algorithm has the benefits of low computational complexity and fast speed. Still, the tracking effect is not good when dealing with complicated circumstances. This paper proposes a multi-feature fusion target repositioning tracking algorithm for the target tracking problem in complex environments. First, a multi-feature weighted fusion algorithm is presented. Since each feature has different advantages in different environments, we combine HOG, CN, ULBP, and image edge features and use the weighted coefficient method to adaptively fuse each feature component. Second, to address the target occlusion problem, an occlusion judgment mechanism is introduced, and the target is re-located by fusion weighted filtering. Third, the scale pool is established, and the scale filter is trained by the classification search method. Finally, an adaptive model update strategy is proposed. We conduct comparison experiments with current mainstream algorithms on the publicly available datasets OTB-2015, VOT2018, UAV123, and TColor-128, respectively, and the experimental results show that our proposed algorithm is more robust in complex scenarios.

Radial Velocity Estimation of Ships on Open Sea in the Azimuth Multichannel SAR System

The azimuth multichannel synthetic aperture radar (SAR) system can meet the requirements of high resolution and wide swath (HRWS) simultaneously, which overcomes the constraint of the traditional single-channel SAR. However, for a moving target illuminated by the azimuth multichannel SAR system, its radial velocity will lead to ambiguous components and mislocation in the image. Therefore, the radial velocity estimation plays an important role in improving the image quality and moving target relocation, especially for large ships on the open sea. However, as the pulse repetition frequency of a single channel is less than the Doppler spectrum, the traditional velocity estimation methods working in the image domain are out of action. This article suggests an idea that the issue of velocity estimation is converted into that of the linear phase errors estimation combining the linear fitting method, and it is assumed that the target has been already detected before applying the velocity estimation algorithms. To estimate the linear phase errors, two algorithms operating in the Doppler domain are introduced and compared, namely the subspace-based method and the modified frequency correlation method. The advantages of the proposed approaches are free from iteration operation and high accuracy. Besides, the effectiveness of the methods is demonstrated via simulation data and GaoFen-3 real data from ultra-fine stripmap mode. Finally, this article analyzes the velocity estimation accuracy of the two methods and the influence of channel imbalance through substantial experiments.

Automatic Guidance (and Misguidance) of Visuospatial Attention by Acquired Scene Memory: Evidence From an N1pc Polarity Reversal

Visual search is facilitated when the target is repeatedly encountered at a fixed position within an invariant (vs. randomly variable) distractor layout—that is, when the layout is learned and guides attention to the target, a phenomenon known as contextual cuing. Subsequently changing the target location within a learned layout abolishes contextual cuing, which is difficult to relearn. Here, we used lateralized event-related electroencephalogram (EEG) potentials to explore memory-based attentional guidance (N = 16). The results revealed reliable contextual cuing during initial learning and an associated EEG-amplitude increase for repeated layouts in attention-related components, starting with an early posterior negativity (N1pc, 80–180 ms). When the target was relocated to the opposite hemifield following learning, contextual cuing was effectively abolished, and the N1pc was reversed in polarity (indicative of persistent misguidance of attention to the original target location). Thus, once learned, repeated layouts trigger attentional-priority signals from memory that proactively interfere with contextual relearning after target relocation.

Parallel Dual Networks for Visual Object Tracking

Visual Object Tracking plays an essential role in solving many basic problems in computer vision. In order to improve the tracking accuracy, the previous methods have prevented tracking failures from occurring by improving the ability to describe the target. However, few of them consider ways to relocate and track the target after a tracking failure. In this paper, we propose the use of a parallel dual network for visual object tracking. This is constructed from two networks and an adjustment module to enable judgement of tracking failures, as well as target relocation and tracking. Firstly, we employ the Siamese matching method and correlation filter method to build tracking network and inspecting network. Both networks track the target simultaneously to obtain two tracking results. Secondly, an adjustment module is constructed, which compares the overlap ratio of the two tracking results with a set threshold, then fuses them or selects the best one. Finally, the fusion or selection result is output and the tracker is updated. We perform comprehensive experiments on five benchmarks: VOT2016, UAV123, Temple Color-128, OTB-100 and OTB-50. The results demonstrate that, compared with other state-of-the-art algorithms, the proposed tracking method improves tracking precision while maintaining real-time performance.

Efficient knowledge‐aided target relocation algorithm for airborne radar

This letter addresses the problem of target relocation in wide-area ground moving target indication system. The antenna pattern information is exploited as prior knowledge to enhance the performance of airborne radar. Firstly, the ideal target relocation model for moving targets based on the dual-channel radar system is analysed and the mismatch model of the receiving channel is constructed. Then the reason for poor relocation performance is analysed and the channel error model is constructed. Based on this fact, the problem of target relocation is converted into a problem of channel phase estimation. Moreover, a least-squares method is utilised to estimate the phase error by exploiting the real antenna pattern information and a modified mono-pulse curve (MPC) is derived. Finally, the real geometry location of the moving target can be achieved with the modified MPC. Airborne radar experiments are given to verify the effectiveness of the proposed algorithm.

Contextual cueing in older adults: Slow initial learning but flexible use of distractor configurations

ABSTRACTLearned spatial regularities can efficiently guide visual search. This effect has been extensively studied using the contextual cueing paradigm. We investigated age-related changes in the initial learning of contextual configurations and the relearning after target relocation. Younger and older participants completed a contextual cueing experiment on two days. On day one, they were tested with a standard contextual cueing task. On day two, for the repeated displays the location of the targets was moved while keeping the distractor configurations unchanged. Older participants developed a reliable contextual cueing effect but the emergence of this effect required more repetitions compared to younger individuals. Contextual cueing was apparent quickly after target relocation in younger and older participants. Especially in older adults, the fast updating might be due to learned distractor-distractor associations rather than the updating of target-distractor configurations.

Contextual Relearning Following Target Relocation in Visual Search

Contextual Relearning Following Target Relocation in Visual Search

Sparsity-Driven GMTI Processing Framework With Multichannel SAR

This paper presents a processing framework to separate moving targets from the clutter, under multichannel synthetic aperture radar (SAR) scenarios, and addresses the moving target imaging and velocity estimation problems for ground moving target indication (GMTI) applications. A practical implementation is introduced to break the SAR/GMTI problem into two processing stages, and the sparsity of the moving targets in the observed scene is exploited throughout the stages. The two-stage process extracts the moving targets from the monitored region via a sparsity-based iterative decomposition algorithm and subsequently estimates the complete velocity vectors of moving targets by enforcing sparsity constraints. The model is sufficiently versatile to incorporate digital elevation map information, which further improves the moving target relocation accuracy. The effectiveness of the presented framework is demonstrated using the Air Force Research Laboratory Gotcha GMTI challenge data.

First Spaceborne SAR-GMTI Experimental Results for the Chinese Gaofen-3 Dual-Channel SAR Sensor.

In spaceborne synthetic aperture radar (SAR) sensors, it is a challenging task to detect ground slow-moving targets against strong clutter background with limited spatial channels and restricted pulse repetition frequency (PRF). In this paper, we evaluate the image-based dual-channel SAR-ground moving target indication (SAR-GMTI) workflow for the Gaofen-3 SAR sensor and analyze the impact of strong azimuth ambiguities on GMTI when the displaced phase center antenna (DPCA) condition is not fully satisfied, which has not been demonstrated yet. An effective sliding window design technique based on system parameters analysis is proposed to deal with azimuth ambiguities and reduce false alarm. In the SAR-GMTI experiments, co-registration, clutter suppression, constant false alarm rate (CFAR) detector, vector velocity estimation and moving target relocation are analyzed and discussed thoroughly. With the real measured data of the Gaofen-3 dual-channel SAR sensor, the GMTI capability of this sensor is demonstrated and the effectiveness of the proposed method is verified.

A Knowledge-Based Target Relocation Method for Wide-Area GMTI Mode

This letter deals with the issue of moving-target relocation in wide-area ground moving-target indication for dual-channel radar systems. Due to channel mismatch, along-track baseline error, the existence of across-track baseline, etc., it is difficult to accurately relocate the detected targets. In this letter, we propose a new knowledge-based (KB) method for target relocation. The key idea of the proposed method is to use such knowledge that a moving target and its neighboring clutter, which is situated at the same azimuth angular position and the same range cell as this target in the observed scene, have the same interferometric phase. This new KB method, as an indirect one, does not employ the conventional relocation formula and therefore is not influenced by most of (if not all) interferometric phase errors. Experimental results from real radar data demonstrate a fairly high degree of target relocation accuracy.

SU-FF-J-90: Target Relocalization Accuracy and PTV Margin Verification Using Three-Dimensional Cone-Beam Computed Tomography (CBCT) in Stereotactic Body Radiotherapy (SBRT) of Lung Cancers

Purpose: To assess the target relocalization accuracy in respect to the bony structures using daily CBCT and thus to validate the planning target volume (PTV) margin used in the lung SBRT. Methods and Materials: All patients underwent 4D CT scanning in preparation for lung SBRT. The internal target volume (ITV) is outlined from the reconstructed 4D data using the maximum-intensity projection (MIP) algorithm. The clinical target volume (CTV) is defined as the ITV plus 3 mm margin and an additional 3 mm margin is added to the CTV to make the PTV. Conformal treatment planning is performed on the helical images, to which the MIP images were fused. Prior to each treatment, CBCT is used to align with the simulation CT (helical) based on bony anatomy. The necessary shifts are recorded. The treating physician then checks and modifies the alignment based on target relocalization within the PTV (soft tissue alignment). The final shifts are derived based on the soft tissue alignment. Two sets of shifts are compared here for the purpose of the study. Results: For 8 consecutive patients, treating 9 targets for a total of 36 fractions, it was found that the treatment setup errors (based on bony anatomy alignment) are 0.23±0.47 cm in the anterior-posterior (AP), 0.06±0.5 cm in the lateral (Lat), and 0.18±0.39 cm in the superior-inferior (S-I) directions, respectively. After the setup error correction, the targets were found to be within the PTV margin with the average shifts of 0.064±0.64 cm in AP, 0.089±0.41 cm in Lat, and 0.21±0.38 cm in S-I directions, respectively. Conclusions: For this patient population, the target relocation accuracy is generally satisfactory after patient setup error correction. This demonstrates that the PTV margin designed based on the ITV outlined on MIP images is appropriate to account for intra and inter-fractional tumor motions.