Nearby contours abolish the binocular advantage

That binocular viewing confers an advantage over monocular viewing for detecting isolated low luminance or low contrast objects, has been known for well over a century; however, the processes involved in combining the images from the two eyes are still not fully understood. Importantly, in natural vision, objects are rarely isolated but appear in context. It is well known that nearby contours can either facilitate or suppress detection, depending on their distance from the target and the global configuration. Here we report that at close distances collinear (but not orthogonal) flanking contours suppress detection more under binocular compared to monocular viewing, thus completely abolishing the binocular advantage, both at threshold and suprathreshold levels. In contrast, more distant flankers facilitate both monocular and binocular detection, preserving a binocular advantage up to about four times the detection threshold. Our results for monocular and binocular viewing, for threshold contrast discrimination without nearby flankers, can be explained by a gain control model with uncertainty and internal multiplicative noise adding additional constraints on detection. However, in context with nearby flankers, both contrast detection threshold and suprathreshold contrast appearance matching require the addition of both target-to-target and flank-to-target interactions occurring before the site of binocular combination. To test an alternative model, in which the interactions occur after the site of binocular combination, we performed a dichoptic contrast matching experiment, with the target presented to one eye, and the flanks to the other eye. The two models make very different predictions for abutting flanks under dichoptic conditions. Interactions after the combination site predict that the perceived contrast of the flanked target will be strongly suppressed, while interactions before the site predict the perceived contrast will be more or less veridical. The data are consistent with the latter model, strongly suggesting that the interactions take place before the site of binocular combination.

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

It is crucial for unmanned surface vessels (USVs) to detect and track surrounding vessels in real time to avoid collisions at sea. However, the harsh maritime environment poses great challenges to multitarget tracking (MTT). In this paper, a novel tracking by detection framework that integrates the multimodel and multicue (M3C) pipeline is proposed, which aims at improving the detection and tracking performance. Regarding the multimodel, we predicted the maneuver probability of a target vessel via the gated recurrent unit (GRU) model with an attention mechanism, and fused their respective outputs as the output of a kinematic filter. We developed a hybrid affinity model based on multi cues, such as the motion, appearance, and attitude of the ego vessel in the data association stage. By using the proposed ship re-identification approach, the tracker had the capability of appearance matching via metric learning. Experimental evaluation of two public maritime datasets showed that our method achieved state-of-the-art performance, not only in identity switches (IDS) but also in frame rates.

Dupuytren’s disease: using keystone techniques to improve vascular dynamics

Background: Palmar defects arising from surgical correction for Dupuytren’s disease can be surgically corrected using skin grafts. This article describes the applications of keystone principles as an alternative to John Hueston’s firebreak graft, popularised in the mid-1980s. Method: In 2003, I introduced the principle of a fenestrated, full-thickness graft to optimise graft-take and expedite healing, offering an alternative for the management of palmar defects created by surgical release. Results: The combination of reliable hypervascularity with a pain-free postoperative phase, characteristic of KPIF, ensures an easy recovery with early commencement of hand therapy. With minimal vascular complications (apart from clinical cases where diabetes and smoking are factors) the overall surgical outcome gives an aesthetic appearance matching surrounding tissues. Conclusion: Island flaps based on the keystone principle improve vascularity resulting in minimal complications with healing. Recurrence of Dupuytren’s disease following this technique has not been observed to date.

Indoor Topological Localization Using a Visual Landmark Sequence

This paper presents a novel indoor topological localization method based on mobile phone videos. Conventional methods suffer from indoor dynamic environmental changes and scene ambiguity. The proposed Visual Landmark Sequence-based Indoor Localization (VLSIL) method is capable of addressing problems by taking steady indoor objects as landmarks. Unlike many feature or appearance matching-based localization methods, our method utilizes highly abstracted landmark sematic information to represent locations and thus is invariant to illumination changes, temporal variations, and occlusions. We match consistently detected landmarks against the topological map based on the occurrence order in the videos. The proposed approach contains two components: a convolutional neural network (CNN)-based landmark detector and a topological matching algorithm. The proposed detector is capable of reliably and accurately detecting landmarks. The other part is the matching algorithm built on the second order hidden Markov model and it can successfully handle the environmental ambiguity by fusing sematic and connectivity information of landmarks. To evaluate the method, we conduct extensive experiments on the real world dataset collected in two indoor environments, and the results show that our deep neural network-based indoor landmark detector accurately detects all landmarks and is expected to be utilized in similar environments without retraining and that VLSIL can effectively localize indoor landmarks.

A parametric texture model based on deep convolutional features closely matches texture appearance for humans

Our visual environment is full of texture—“stuff” like cloth, bark or gravel as distinct from “things” like dresses, trees or paths—and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parameteric model of texture appearance (CNN model) that uses the features encoded by a deep convolutional neural network (VGG-19) with two other models: the venerable Portilla and Simoncelli model (PS) and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery (“parafoveal”) and when observers were able to make eye movements to all three patches (“inspection”). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler PS model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the PS model (9 compared to 4 textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesise textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions.