In this paper, we present an inexpensive system for diverless video capture and fast image stitching of image frames for rapid reef assessment of shallow coral reefs. Our system has two main components: 1) Teardrop, a...
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In this paper, we present an inexpensive system for diverless video capture and fast image stitching of image frames for rapid reef assessment of shallow coral reefs. Our system has two main components: 1) Teardrop, a...
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In this paper, we present an inexpensive system for diverless video capture and fast image stitching of image frames for rapid reef assessment of shallow coral reefs. Our system has two main components: 1) Teardrop, a boat-towable, winged hull apparatus designed to house a commercial digital camera, and 2) a mosaicking algorithm to stitch the coral reef video into mosaics for further appreciation and analysis. The captured reef video is then separated into image frames which are to be stitched sequentially using Fast Image Labeling. The overlap between image frames is estimated using Single-Step DFT, an efficient sub-pixel estimation algorithm. The estimated overlap is used to compute for the area to be added to the mosaic space. The overlapping section between succeeding image-pairs are stitched along a seam determined by a minimal-cost path using dynamic programming. The visibility of the seam boundaries is further minimized by utilizing blending on multi-resolution splines. Experimental results on automated reef mosaics creation from actual coral reef video taken using Teardrop shows the performance of the system described. The main contribution of this work is the demonstration of a rapid reef visualization system using a diverless system and commercially available, non-research-grade imaging equipment.
In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for rec...
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In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations. In accordance with HTM's biological inspiration, human vision mechanisms can be used to preprocess the input images. Therefore, the input images undergo a saliency computation step, revealing the plausible information of the scene, where a human might fixate. The adoption of the saliency detection module releases the HTM network from memorizing redundant information and augments the classification accuracy. The efficiency of the proposed framework has been experimentally evaluated in the ETH-80 dataset, and the classification accuracy has been found to be greater than other HTM systems.
Mobile robots dedicated in post-disaster missions should be capable of moving arbitrarily in unknown cluttered environments so as to accomplish their assigned security task. The paper in hand describes such an agent e...
Mobile robots dedicated in post-disaster missions should be capable of moving arbitrarily in unknown cluttered environments so as to accomplish their assigned security task. The paper in hand describes such an agent equipped with collision risk assessment capabilities, while it is able to trace an obstacle-free path in the scene as well. The robot exploits machine learning techniques for the traversability evaluation of the environment by making use of geometrical features, which derive from a postprocessing step of the depth map, obtained by an RGBD sensor. Then, the traversable scenes, are assessed by the likelihood the robot to collide on any arbitrary direction in front of it. Besides, the collision risk likelihood is combined with a path tracing algorithm based on Cellular Automata so that an obstacle-free route is then detected. The proposed method has been examined for several indoor scenarios revealing remarkable efficiency.
This paper presents a method to build large-scale mosaics adapted to underwater sonar imagery. By assuming a simplified imaging model, we propose to address the registrations between images using Fourier-based methods...
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ISBN:
(纸本)9781467317375
This paper presents a method to build large-scale mosaics adapted to underwater sonar imagery. By assuming a simplified imaging model, we propose to address the registrations between images using Fourier-based methods which, unlike feature-based methods, prove well suited to handle the characteristics of forward-looking sonar images, such as low resolution, noise, occlusions and moving shadows. The registration between spatially and temporally distant images resulting from loop-closing situations or registrations in featureless areas are feasible, overcoming the main difficulties of feature-based methods. The problem is cast as a pose-based graph optimization, taking into account the uncertainties of the pairwise registrations and being able to incorporate navigation information. After the optimization, a consistent mosaic from different tracklines is generated with increased resolution and higher signal-to-noise ratio than the original images, while the vehicle motion in x,y and heading is also estimated.
Wireless sensor networks (WSNs) have attracted a great deal of research due to their wide-range of potential applications. Sensor deployment and coverage problems are their important issues. This article briefly intro...
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The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural *** by this fact,we propose a biologically plausible approach for natural scene im...
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The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural *** by this fact,we propose a biologically plausible approach for natural scene image *** approach consists of one visual perception model and two visual cognition *** visual perception model,composed of two steps,is used to extract discriminative features from natural scene *** the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure *** the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure *** we design a cognitive feedback model to realize adaptive optimization for the visual perception *** last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene *** on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.
作者：
Duo ChenJun ChengDacheng TaoCollege of Communication Engineering
Chongqing University Chongqing 400044 China. He is also with the Shenzhen Key Laboratory of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences. Shenzhen Institutes of Advanced Technology
Chinese Academy of Sciences Shenzhen 518055 China. He is also with the Chinese University of Hong Kong and Guangdong Provincial Key Laboratory of Robotics and Intelligent System. Center for Quantum Computation and Intelligent System
Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia.
To facilitate human-robot interactions, human gender information is very important. Motivated by the success of manifold learning for visual recognition, we present a novel clustering-based discriminative locality ali...
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ISBN:
(数字)9781467317368
ISBN:
(纸本)9781467317375
To facilitate human-robot interactions, human gender information is very important. Motivated by the success of manifold learning for visual recognition, we present a novel clustering-based discriminative locality alignment (CDLA) algorithm to discover the low-dimensional intrinsic submanifold from the embedding high-dimensional ambient space for improving the face gender recognition performance. In particular, CDLA exploits the global geometry through k-means clustering, extracts the discriminative information through margin maximization and explores the local geometry through intra cluster sample concentration. These three properties uniquely characterize CDLA for face gender recognition. The experimental results obtained from the FERET data sets suggest the superiority of the proposed method in terms of recognition speed and accuracy by comparing with several representative methods.
Safe operation of a motor vehicle requires awareness of the current traffic situation as well as the ability to predict future maneuvers. In order to provide an intelligent vehicle the ability to make predictions, thi...
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Safe operation of a motor vehicle requires awareness of the current traffic situation as well as the ability to predict future maneuvers. In order to provide an intelligent vehicle the ability to make predictions, this work proposes a framework for understanding the driving situation based on vehicle mounted vision sensors. Vehicles are tracked using Kalman filtering based on a vision-based system that detects and tracks using a combination of monocular and stereo-vision. The vehicles' full trajectories are recorded, and a data-driven learning framework has been applied to automatically learn surround behaviors. By learning based on observations, the ADAS system is being trained by experience. Learned trajectories have been compared between dense and free-flowing traffic conditions. Preliminary experimental results using real-world multi-lane highways show the basic promise of this approach. Future research directions are discussed.
Safe and efficient navigation of robotic swarms is an important research problem. One of the main challenges in this area is to avoid congestion, which usually happens when large groups of robots share the same enviro...
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Safe and efficient navigation of robotic swarms is an important research problem. One of the main challenges in this area is to avoid congestion, which usually happens when large groups of robots share the same environment. In this paper, we propose the use of hierarchical abstractions in conjunction with simple traffic control rules based on virtual forces to avoid congestion in swarm navigation. We perform simulated and real experiments in order to study the feasibility and effectiveness of the proposed algorithm. Results show that our approach allows the swarm to navigate without congestions in a smooth and coherent fashion, being suitable for large groups of robots.
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