We have developed a ground vehicle capable of maneuvering in an open environment negotiating outdoor obstacle course autonomously carrying a payload by finding colored lanes, obstructions and navigating through GPS wa...
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ISBN:
(纸本)9780819499424
We have developed a ground vehicle capable of maneuvering in an open environment negotiating outdoor obstacle course autonomously carrying a payload by finding colored lanes, obstructions and navigating through GPS waypoints. In this paper, we will be discussing the hardware components like mechanical and electrical design, various sensors used and software components of the vehicle like imageprocessing, environment mapping and navigation algorithms. The vehicle uses its sensors to develop some limited understanding of the environment, which is then used by control algorithms to determine the next action to take in the context of a human provided mission goal.
Shape-from-focus (SFF) is extensively used in imageprocessing for obtaining shape-maps using a sequence of images of a scene captured from same view point with different camera focus settings. Many focus measure oper...
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ISBN:
(纸本)1595930361
Shape-from-focus (SFF) is extensively used in imageprocessing for obtaining shape-maps using a sequence of images of a scene captured from same view point with different camera focus settings. Many focus measure operators have been proposed in the literature for SFF applications and their relative performance depends on the camera capabilities and scene properties. In this work, we propose a novel shape estimation method which fuses individual shape estimates obtained by multiple focus measures, and reconstructs an improved shape map in a patch-wise manner, within a sparse representation (SR) framework. The SR framework involves an over-complete dictionary containing shape patches, the linear combination of which is used to reconstruct each out-put shape patch. The experimental results demonstrate that the proposed approach estimates superior shape maps in both synthetic and real scenes as compared to the estimates obtained by individual focus measures. Copyright 2014 ACM.
In this paper, we present a novel method for 3D reconstruction from epipolar plane (EP) representation of images and surface fitting for multiview or lightfield images. The proposed method detects parallelograms in EP...
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ISBN:
(纸本)1595930361
In this paper, we present a novel method for 3D reconstruction from epipolar plane (EP) representation of images and surface fitting for multiview or lightfield images. The proposed method detects parallelograms in EP images using mean shift segmentation. The slopes of the parallel lines along the left and right boundaries of the parallelograms are computed with Hough transform to estimate depth. In addition we also introduce a new concept of estimating depth from multiple EP images(diagonal EP images), making the resultant depth map denser. Multi-view depth maps are then combined and surface fitting is done to obtain the final depth map. Our method uses simple techniques to construct a quality depth map without being computationally demanding. Copyright 2014 ACM.
A limitation of using the discriminant analysis techniques for dimension reduction in image classification tasks is that the number of classes is significantly smaller than the dimension of global feature vectors used...
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ISBN:
(纸本)1595930361
A limitation of using the discriminant analysis techniques for dimension reduction in image classification tasks is that the number of classes is significantly smaller than the dimension of global feature vectors used to represent the images. In such cases, the reduced dimension is restricted by the number of classes and that reduced dimension may not adequately capture the necessary discriminatory information for classification. We propose a method to perform the discriminant analysis using the concept level labels for local feature vectors extracted from the blocks of an image. The dimension reduction is carried out on the local feature vectors. We consider the indices of the clusters of local feature vectors of all the images as the unnamed concept level labels. As the number of concept level labels is larger than the number of class level labels, the reduced dimension for local feature vectors can be higher than the number of class level labels. We consider the Gaussian mixture model based classifiers and support vector machine based classifiers for image classification using the set of local feature vectors representation of images. Results of experimental studies on image classification for MIT-8 and Vogel-6 image datasets demonstrate the effectiveness of proposed concept level discriminant analysis techniques for dimension reduction. Copyright 2014 ACM.
Long range surveillance videos are often distorted by random perturbations of the optical pathways caused by atmospheric turbulence. While humans can easily perceive and separate the moving objects from the turbulent ...
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ISBN:
(纸本)1595930361
Long range surveillance videos are often distorted by random perturbations of the optical pathways caused by atmospheric turbulence. While humans can easily perceive and separate the moving objects from the turbulent motion, it is still a challenge for vision systems. In this paper, we present a near real-time approach to detecting moving objects in the presence of turbulence. As a first step, our method learns the dynamics of the latent turbulence and extracts an approximate foreground. Statistical analysis of the foreground object properties is used to eliminate noise due to turbulence preserving only the true moving objects. Our approach also results in a stable background with minimal turbulence. We tested our method on a wide range of scenarios corrupted by various levels of turbulence. Compared with state of the art, our results are quite promising. Copyright 2014 ACM.
Motion blur is a common phenomenon in the presence of camera shake or object motion. In this paper, we deal with the challenging situation of underwater imaging. Specifically, we assume a static camera looking vertica...
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ISBN:
(纸本)1595930361
Motion blur is a common phenomenon in the presence of camera shake or object motion. In this paper, we deal with the challenging situation of underwater imaging. Specifically, we assume a static camera looking vertically down-wards at a scene but through a flowing water surface. The source of motion blur is due to the dynamic medium between the scene and the camera. Under reasonable assumptions, we establish that the motion blur induced by commonly observed fluid flows can serve as a valuable cue for inferring the underlying depth layers of the scene. We validate our approach with synthetic and real examples. Copyright 2014 ACM.
Range-image super-resolution has evolved in recent years to improve the images acquired by low-resolution range-cameras. In this regard, some local filtering based approaches are quite popular as they achieve reasonab...
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ISBN:
(纸本)1595930361
Range-image super-resolution has evolved in recent years to improve the images acquired by low-resolution range-cameras. In this regard, some local filtering based approaches are quite popular as they achieve reasonable quality range-images while maintaining high computational efficiency. In this work, we propose a novel and improved local approach, which is inspired by the popular Guided image Filtering method, that employs information from an associated color image for the task of range-image super-resolution. Our approach accounts for consideration of the content of both color image and range image explicitly, to drive the enhancement process. We show that our filter reduces noise for noisy range-images along with better edge enhancement, especially for higher up-sampling factors. Our experimentation also demonstrate that our approach performs better than other prominent local filtering approaches both in terms of depth precision and spatial resolution without any considerable increase in computational time. Copyright 2014 ACM.
In this paper, we propose a novel method for image texture characterization. Characterization is governed by simple perceptual variations in relative orientations in terms of either no variations present or variations...
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ISBN:
(纸本)1595930361
In this paper, we propose a novel method for image texture characterization. Characterization is governed by simple perceptual variations in relative orientations in terms of either no variations present or variations present as row specific, column specific or diagonal specific. This generalization is obtained by modeling the input as a whole or image blocks depending on the broader or narrow coverage respectively. Most of the texture characterization is done either keeping a specific domain (synthetic or natural images specific to a category) or is application specific (segmentation on specific benchmark dataset or image retrieval). Contrary to this, our method is not biased towards any domain or application;rather it acts as a pre-processing step for guiding towards locating both non-textural and orientation specific textural image blocks. The proposed method quantifies the texture-tonal characterization of an image or image blocks using statistical ANOVA grading system. Once the grading for abstraction is assigned for both image as a whole and also for image blocks, the decision as to which higher-level algorithms need to be implemented on which block will become easier. The proposed method can be considered to be a three stage process - progressive sampling, image partitioning in blocks, ANOVA analysis and grading. Copyright 2014 ACM.
Video interest points, in combination with local appearance descriptors, are used for human action recognition. Most of the previously proposed video interest point detectors are straightforward extensions of some ima...
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ISBN:
(纸本)1595930361
Video interest points, in combination with local appearance descriptors, are used for human action recognition. Most of the previously proposed video interest point detectors are straightforward extensions of some image interest point detector or the other. These methods treat the temporal dimension (inter-frame) similar to the spatial dimensions (intra-frame). We argue that certain unique properties of the temporal dimension beg a different treatment. We propose an interest point detector based on vector calculus of optical flow to take advantage of the unique properties of the temporal dimension. Compared to previously proposed methods, the proposed method exhibits higher repeatability (robustness) and lower displacement (stability) of interest points under two common video transformations tested-video compression and spatial scaling. It also shows competitive action recognition performance when paired with appropriate feature descriptors in a bag of features model. Copyright is held by the authors.
In this paper, we propose a novel image completion method using transform domain patch approximation method and kd-tree based nearest neighbor field (NNF) computation in multiscale fashion. In NNF, two important proce...
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ISBN:
(纸本)1595930361
In this paper, we propose a novel image completion method using transform domain patch approximation method and kd-tree based nearest neighbor field (NNF) computation in multiscale fashion. In NNF, two important processes are initialization of image target region and candidate patch searching method. Most of the previous techniques choose random initialization with arbitrary source image pixels or garbage values. It may misguide to image completion process and allow to select the bad candidate patches. We solve the problem using higher order singular value decomposition (HOSVD). It smoothly generates information in the target region enhancing the edge sharpness which helps to complete image structure quite successfully. It also preserves texture color in the target region. To overcome the problem of patch searching, we introduce robust kd-tree search method in our patch approximation step. Our experiment and analysis shows that the proposed method can be applied to the various types of image editing tools for natural images. Copyright 2014 ACM.
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