Just around the corner: Acoustic mirroring enables echolocating bats to detect occluded objects

The study of illusory brightness and contour phenomena has become an important tool in modern brain research. Gestalt, cognitive, neural, and computational approaches are reviewed and their explanatory powers are discussed in the light of empirical data. Two well-known phenomena of illusory form are dealt with, the Ehrenstein illusion and the Kanizsa triangle. It is argued that the gap between the different levels of explanation, bottom—up versus top—down, creates scientific barriers which have all too often engendered unnecessary debate about who is right and who is wrong. In this review of the literature we favour an integrative approach to the question of how illusory form is derived from stimulus configurations which provide the visual system with seemingly incomplete information. The processes that can explain the emergence of these phenomena range from local feature detection to global strategies of perceptual organisation. These processes may be similar to those that help us restore partially occluded objects in everyday vision. To understand better the Ehrenstein and Kanizsa illusions, it is proposed that different levels of analysis and explanation are not mutually exclusive, but complementary. Theories of illusory contour and form perception must, therefore, take into account the underlying neurophysiological mechanisms and their possible interactions with cognitive and attentional processes.

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Analysis of Cluttered Scenes Using an Elastic Matching Approach for Stereo Images

Neural Computation ◽

10.1162/neco.2006.18.6.1441 ◽

2006 ◽

Vol 18 (6) ◽

pp. 1441-1471 ◽

Cited By ~ 3

Author(s):

Christian Eckes ◽

Jochen Triesch ◽

Christoph von der Malsburg

Keyword(s):

Graph Matching ◽

Recognition Performance ◽

Stereo Image ◽

Matching Algorithm ◽

Automatic Interpretation ◽

Color Features ◽

Occluded Objects ◽

Elastic Graph Matching ◽

Image Pairs ◽

Object Models

We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.

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Grasping Partly Occluded Objects: Effects of Global Stimulus Information on Action

Perception ◽

10.1068/p6158 ◽

2009 ◽

Vol 38 (2) ◽

pp. 200-214 ◽

Cited By ~ 3

Author(s):

Janneke Lommertzen ◽

Rob van Lier ◽

Ruud G J Meulenbroek

Keyword(s):

Stimulus Information ◽

Occluded Objects

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CenterTrack3D: Improved CenterTrack more Suitable for 3D Objects

Journal of Autonomous Vehicles and Systems ◽

10.1115/1.4050863 ◽

2021 ◽

pp. 1-32

Author(s):

Lipeng Gu ◽

Shaoyuan Sun ◽

Xunhua Liu ◽

Xiang Li

Keyword(s):

Object Tracking ◽

Research Field ◽

Unmanned Vehicles ◽

Current Frame ◽

3D Objects ◽

Detection And Tracking ◽

Tracking Algorithms ◽

Occluded Objects ◽

Previous Frame ◽

3D Information

Abstract Compared with 2D multi-object tracking algorithms, 3D multi-object tracking algorithms have more research significance and broad application prospects in the unmanned vehicles research field. Aiming at the problem of 3D multi-object detection and tracking, in this paper, the multi-object tracker CenterTrack, which focuses on 2D multi-object tracking task while ignoring object 3D information, is improved mainly from two aspects of detection and tracking, and the improved network is called CenterTrack3D. In terms of detection, CenterTrack3D uses the idea of attention mechanism to optimize the way that the previous-frame image and the heatmap of previous-frame tracklets are added to the current-frame image as input, and second convolutional layer of the output head is replaced by dynamic convolution layer, which further improves the ability to detect occluded objects. In terms of tracking, a cascaded data association algorithm based on 3D Kalman filter is proposed to make full use of the 3D information of objects in the image and increase the robustness of the 3D multi-object tracker. The experimental results show that, compared with the original CenterTrack and the existing 3D multi-object tracking methods, CenterTrack3D achieves 88.75% MOTA for cars and 59.40% MOTA for pedestrians and is very competitive on the KITTI tracking benchmark test set.

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