In this work, we propose an object recognitionstrategy in a domestic environment. Our contribution is to use low-level features extracted from images with high-level concepts generated from an ontology of domestic ob...
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ISBN:
(纸本)9781479948888
In this work, we propose an object recognitionstrategy in a domestic environment. Our contribution is to use low-level features extracted from images with high-level concepts generated from an ontology of domestic objects to get richer decision. It consists in developing a semantic classification by providing for a white cane user the class of the obstacle and the scene in which it is located. The classification is performed with a decision tree that provides a better recognition rate than SVM. The combination of color and texture features resolves the ambiguities of shape features for some objects that have similar shape.
Electronic device functionality and dependability can be severely compromised by soldering flaws in printed circuit boards (PCBs). Effective quality control in manufacturing processes depends on automated flaw identif...
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Nowadays the biometric identification is a part of our life and there are reasons for determining the possibility of applying different methods of a user identification in noisy channel case by using the machine learn...
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Automatic feature identification from orbital imagery would be of wide use in planetary science. For geo scientific applications, automatic shape-based feature detection offers a fast and non-subjective means of ident...
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ISBN:
(纸本)9783642240546
Automatic feature identification from orbital imagery would be of wide use in planetary science. For geo scientific applications, automatic shape-based feature detection offers a fast and non-subjective means of identifying geological structures within data. Most previously published examples of circular feature detection for geo scientific applications aimed to identify impact craters from optical or topographic data. Various techniques used include the texture analysis, template matching, and machinelearning. In this paper, we propose a new method for the extraction of features from the planetary surface, based on the combination of several imageprocessing techniques, including a shadow removal, watershed segmentation and the Circular Hough Transform (CUT). The original edge map of craters is detected by canny operator. In most literatures Hough transform is generally used for crater detection but we have added a shadow removal which includes a novel color image fusion method, based on the multi-scale Retinex (MSR) and discrete wavelet transform (DWT), is proposed. This proposed method is capable of detecting partially visible craters, and overlapping craters.
Generation of high density depth values of the driving environment is indispensable for autonomous driving. stereo vision is one of the practical and effective methods to generate these depth values. However, the accu...
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ISBN:
(纸本)9781728188089
Generation of high density depth values of the driving environment is indispensable for autonomous driving. stereo vision is one of the practical and effective methods to generate these depth values. However, the accuracy of the stereo vision is limited by texture-less regions, such as sky and road areas, and repeated patterns in the image. To overcome these problems, we propose to enhance the stereo generated depth by incorporating prior information of the driving environment. Prior information, generated by deep learning-based U-Net model, is utilized in a novel post-processing mathematical framework to refine the stereo generated depth. The proposed mathematical framework is formulated as an optimization problem, which refines the errors due to texture-less regions and repeated patterns. Owing to its mathematical formulation, the post-processing framework is not a black-box and is explainable, and can be readily utilized for depth maps generated by any stereo vision algorithm. The proposed framework is qualitatively validated on the acquired dataset and KITTI dataset. The results obtained show that the proposed framework improves the stereo depth generation accuracy.
The proceedings contain 127 papers. The topics discussed include: rough sets in perception-based computing;geometric decision rules for instance-based learning problems;illumination invariant face alignment using mult...
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ISBN:
(纸本)3540305068
The proceedings contain 127 papers. The topics discussed include: rough sets in perception-based computing;geometric decision rules for instance-based learning problems;illumination invariant face alignment using multi-band active appearance model;globally optimal 3D image reconstruction and segmentation via energy minimization techniques;handwritten Bangla digit recongnition using classifier combination through DS technique;effective intrusion type identification with edit distance for HMM-based anomaly detection system;recognition of fault signature patterns using fuzzy logic for prevention of breakdowns in steel continuous casting process;and intelligent learning rules for fuzzy control of a vibrating screen.
Visual Question Answering (VQA) has recently attracted considerable attention from researchers in the trending field of deep learning. The need to improve VQA models by focusing on local regions of images, has resulte...
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ISBN:
(纸本)9781538618578
Visual Question Answering (VQA) has recently attracted considerable attention from researchers in the trending field of deep learning. The need to improve VQA models by focusing on local regions of images, has resulted in the development of various attention models. This paper proposes the Affective Visual Question Answering Network (AVQAN), an attention model that combines the locality of the image features, the question and the mood detected from the specific region of the image to produce an affective answer using a preprocessed image dataset. The experimental results depict that AVQAN enriches the analysis and understanding of images by adding affective information to the answer, while still managing to maintain the accuracy levels within the range of recent ordinary VQA baseline models. The proposed model significantly contributes towards the development of rapidly improving emotion-aware machines that are becoming increasingly vital in everyday life.
Radial spanning is a fast means of identifying "blobs," or clusters of pixels with some common image property. It compares favorably in theory with other methods for detection of blobs, including snakes, win...
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Radial spanning is a fast means of identifying "blobs," or clusters of pixels with some common image property. It compares favorably in theory with other methods for detection of blobs, including snakes, window-based methods, and connected components analysis. Experiments with tracking of faces verify its advantages in actual performance.
A hierarchical framework for document segmentation is proposed as an optimization problem. The model incorporates the dependencies between various levels of the hierarchy unlike traditional document segmentation algor...
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This work presents an innovative system/app designed to assist individuals with visual or hearing impairments in communicating with the outside world using American Sign Language (ASL). By leveraging machinelearning ...
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