3D sensing and visualization of occluded objects

2006 ◽  
Author(s):  
Yong Seok Hwang ◽  
Seung-Hyun Hong ◽  
Bahram Javidi
Keyword(s):  
Occluded Objects ◽  
3D Sensing

Multi-modal Data Analysis and Fusion for Robust Object Detection in 2D/3D Sensing

2020 ◽  
Author(s):  
Jonathan Schierl ◽  
Quinn Graehling ◽  
Theus Aspiras ◽  
Vijay Asari ◽  
Andre Van Rynbach ◽  
...  
Keyword(s):  
Data Analysis ◽  
Object Detection ◽  
Modal Data ◽  
3D Sensing

scholarly journals MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning

2021 ◽  
pp. 1-1
Author(s):  
Jingzhi Hu ◽  
Hongliang Zhang ◽  
Kaigui Bian ◽  
Marco Di Renzo ◽  
Zhu Han ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
3D Sensing

scholarly journals Phenomena of Illusory Form: Can We Bridge the Gap between Levels of Explanation?

Perception ◽  
1995 ◽  
Vol 24 (11) ◽  
pp. 1333-1364 ◽  
Author(s):  
Lothar Spillmann ◽  
Birgitta Dresp
Keyword(s):  
Feature Detection ◽  
Brain Research ◽  
Form Perception ◽  
Integrative Approach ◽  
Levels Of Explanation ◽  
Attentional Processes ◽  
Occluded Objects ◽  
Neurophysiological Mechanisms ◽  
Partially Occluded ◽  
Different Levels

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.

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

2006 ◽  
Vol 18 (6) ◽  
pp. 1441-1471 ◽  
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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