The Research on Rejoining of the Oracle Bone Rubbings Based on Curve Matching

The rejoining of oracle bone rubbings is a fundamental topic for oracle research. However, it is a tough task to reassemble severely broken oracle bone rubbings because of detail loss in manual labeling, the great time consumption of rejoining, and the low accuracy of results. To overcome the challenges, we introduce a novel CFDA&CAP algorithm that consists of the Curve Fitting Degree Analysis (CFDA) algorithm and the Correlation Analysis of Pearson (CAP) algorithm. First, the orthogonalization system is constructed to extract local features based on the curve features analysis. Second, the global feature descriptor is depicted by using coordinate points sequences. Third, we screen candidate curves based on the features as well as the CFDA algorithm, so the search range of the candidates is narrowed down. Finally, image recommendation libraries for target curves are generated by adopting the CAP algorithm, and the rank for each target matching curve generates simultaneously for result evaluation. With experiments, the proposed method shows a good effect in rejoining oracle bone rubbings automatically: (1) it improves the average accuracy rate of curve matching up to 84%, and (2) for a low-resource task, the accuracy of our method has 25% higher accuracy than that of other methods.

TemporalRI: subgraph isomorphism in temporal networks with multiple contacts

Temporal networks are graphs where each edge is associated with a timestamp denoting when two nodes interact. Temporal Subgraph Isomorphism (TSI) aims at retrieving all the subgraphs of a temporal network (called target) matching a smaller temporal network (called query), such that matched target edges appear in the same chronological order of corresponding query edges. Few algorithms have been proposed to solve the TSI problem (or variants of it) and most of them are applicable only to small or specific queries. In this paper we present TemporalRI, a new subgraph isomorphism algorithm for temporal networks with multiple contacts between nodes, which is inspired by RI algorithm. TemporalRI introduces the notion of temporal flows and uses them to filter the search space of candidate nodes for the matching. Our algorithm can handle queries of any size and any topology. Experiments on real networks of different sizes show that TemporalRI is very efficient compared to the state-of-the-art, especially for large queries and targets.

Automatic target matching

Many photogrametric processes require a large number of points to be collected from numerous digital images. It is imperative that these points be collected accurately, so that precise real-world coordinates may be assigned to points captured in the image. To this end, many techniques have been developed to locate, track and identify image targets. This thesis outlines many of these techniques and presents a target matching solution that has been developed in C++, for the subpixel target location program INDMET . The target matching solution is composed of three elements: an epipolar line program, a cross correlation program and a template least squares matching program. The epipolar line program is used to limit the search area in the right image of a given stereo pair, to the vicinity of a single line. The cross correlation program searches this line to locate possible targets and the template least squares matching program is used to determine the target centre of a black and white image target, once it has been located. It was found that these three programs, working together, had between a 20 and 70 percent chance of locating the correct target, depending on the similarity of elliptical targets in each image. Once found, the program could calculate the target centre to an accuracy of approximately 1/10th of a pixel.

Automatic target matching

Many photogrametric processes require a large number of points to be collected from numerous digital images. It is imperative that these points be collected accurately, so that precise real-world coordinates may be assigned to points captured in the image. To this end, many techniques have been developed to locate, track and identify image targets. This thesis outlines many of these techniques and presents a target matching solution that has been developed in C++, for the subpixel target location program INDMET . The target matching solution is composed of three elements: an epipolar line program, a cross correlation program and a template least squares matching program. The epipolar line program is used to limit the search area in the right image of a given stereo pair, to the vicinity of a single line. The cross correlation program searches this line to locate possible targets and the template least squares matching program is used to determine the target centre of a black and white image target, once it has been located. It was found that these three programs, working together, had between a 20 and 70 percent chance of locating the correct target, depending on the similarity of elliptical targets in each image. Once found, the program could calculate the target centre to an accuracy of approximately 1/10th of a pixel.

Attentional Templates Are Sharpened through Differential Signal Enhancement, Not Differential Allocation of Attention

In visual search, the internal representation of the target feature is referred to as attentional template. The attentional template can be broad or precise depending on the task requirements. In singleton search, the attentional template is broad because the target is the only colored element in the display. In feature search, a precise attentional template is required because the target is in a specific color in an array of varied colors. To measure the precision of the attentional template, we used a cue-target paradigm where cueing benefits decrease when the cue color differs from the target color. Consistent with broad and precise attentional templates, the decrease of cueing effects was stronger in feature than in singleton search. Measurements of ERPs showed that the N2pc elicited by the cue decreased with increasing color difference, suggesting that attention was more strongly captured by cues that were similar to the target. However, the cue-elicited N2pc did not differ between feature and singleton search, making it unlikely to reflect the mechanism underlying attentional template precision. Furthermore, there was no evidence for attentional suppression as there was no cue-elicited PD, even in conditions where the cueing benefit turned into a same-location cost. However, an index of signal enhancement, the contralateral positivity, reflected attention template precision. In general, there was sensory enhancement of the stimulus appearing at the cued location in the search display. With broad attentional templates, any stimulus at the cued location was enhanced, whereas enhancement was restricted to target-matching colors with precise attentional templates.

Statistical regularities cause attentional suppression with target-matching distractors

Visual search may be disrupted by the presentation of salient, but irrelevant stimuli. To reduce the impact of salient distractors, attention may suppress their processing below baseline level. While there are many studies on the attentional suppression of distractors with features distinct from the target (e.g., a color distractor with a shape target), there is little and inconsistent evidence for attentional suppression with distractors sharing the target feature. In this study, distractor and target were temporally separated in a cue–target paradigm, where the cue was shown briefly before the target display. With target-matching cues, RTs were shorter when the cue appeared at the target location (valid cues) compared with when it appeared at a nontarget location (invalid cues). To induce attentional suppression, we presented the cue more frequently at one out of four possible target positions. We found that invalid cues appearing at the high-frequency cue position produced less interference than invalid cues appearing at a low-frequency cue position. Crucially, target processing was also impaired at the high-frequency cue position, providing strong evidence for attentional suppression of the cued location. Overall, attentional suppression of the frequent distractor location could be established through feature-based attention, suggesting that feature-based attention may guide attentional suppression just as it guides attentional enhancement.