In this article a Vector Symbolic Architectures is purposed to implement a hierarchical Graph Neuron for memorizing patterns of Persian/Arabic isolated characters. The main challenge in this topic is using Vector Symb...
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Braille-a model introduced to reduce the illiteracy rate among the visually challenged people. There has been a lot of scope for the conversion of English language to Braille but, the problem arises when the masses ar...
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ISBN:
(纸本)9781479984336
Braille-a model introduced to reduce the illiteracy rate among the visually challenged people. There has been a lot of scope for the conversion of English language to Braille but, the problem arises when the masses are unable to understand the communication by the visually challenged people. This paper focuses on the conversion of the Braille code representing Odia language(a language widely spoken in East India) into Odia word as text. For this, imageprocessing using MATLAB technique provides a suitable platform to perform the segmentation of Braille cell for pattern selection and hence, Odia letter and word recognition. Braille Data Base creation acts as a storage system for the process and its accuracy is also tested which is explained in this paper.
作者：
Li, Robert Y.NASA ACE Center
Dept. of Electrical Engineering N.C. A and T State University Greensboro NC 27411
3-D information about an object can be provided by a laser radar sensor through range measurements. Hough transform has been used to detect certain shapes in 2-D images. Here, the concept is extended to handle 3-D las...
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3-D information about an object can be provided by a laser radar sensor through range measurements. Hough transform has been used to detect certain shapes in 2-D images. Here, the concept is extended to handle 3-D laser radar range data for extraction of planar and nonplanar surfaces. The focus of the research is to use advanced imageprocessing techniques for estimating object geometry under 3-D conditions.
The author presents a novel representation of three-dimensional (3D) patterns invariant to similarity transformations based on the third-order correlation. The invariance represents the amount of the apexes of similar...
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The author presents a novel representation of three-dimensional (3D) patterns invariant to similarity transformations based on the third-order correlation. The invariance represents the amount of the apexes of similar triangles in 3D image data. Computer simulation is done to test the invariance with regard to errors due to numerical calculation and to robustness to noise.
We propose a new coding algorithm for binary images based on neighborhood relations. The shape is transformed into a set of representative code vectors (position invariant) by coding each pixel according to the number...
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We propose a new coding algorithm for binary images based on neighborhood relations. The shape is transformed into a set of representative code vectors (position invariant) by coding each pixel according to the number of neighbors in the four directions (north, east, south, west). These neighborhood vectors are then transformed into a set of codes satisfying the boundary condition given by the size of the image where the shape is imbedded. A code reduction scheme is proposed for the purpose of information reduction and generalization of the shape image. Using the digits 1 and 0 of the NIst handwritten segmented characters set we show a preliminary application for patternrecognition.
This paper presents content-based image retrieval frameworks with relevance feedback based on AdaBoostlearning method. As we know relevance feedback (RF) is online process. so we have optimized the learning process b...
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ISBN:
(纸本)9783642240546;9783642240553
This paper presents content-based image retrieval frameworks with relevance feedback based on AdaBoostlearning method. As we know relevance feedback (RF) is online process. so we have optimized the learning process by considering the most positive image selection on each feedback iteration. To learn the system we have used AdaBoost. The main significances of our system are to address the small training sample and to reduce retrieval time. Experiments are conducted on 1856 texture images to demonstrate the effectiveness of the proposed framework. These experiments employed large image databases and combined RCWFs and DT-CWT texture descriptors to represent content of the images.
An analog implementation of a deep machine-learning system for efficient feature extraction is presented in this work. It features online unsupervised trainability and non-volatile floating-gate analog storage. It uti...
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ISBN:
(纸本)9781479909186
An analog implementation of a deep machine-learning system for efficient feature extraction is presented in this work. It features online unsupervised trainability and non-volatile floating-gate analog storage. It utilizes a massively parallel reconfigurable current-mode analog architecture to realize efficient computation, and leverages algorithm-level feedback to provide robustness to circuit imperfections in analog signal processing. A 3-layer, 7-node analog deep machine-learning engine was fabricated in a 0.13 mu m standard CMOS process, occupying 0.36 mm(2) active area. At a processing speed of 8300 input vectors per second, it consumes 11.4 mu W from the 3 V supply, achieving 1x10(12) operation per second per Watt of peak energy efficiency. Measurement demonstrates real-time cluster analysis, and feature extraction for patternrecognition with 8-fold dimension reduction with an accuracy comparable to the floating-point software simulation baseline.
This paper provides an empirical study for feature learning based on induction. We encode image data into first-order expressions and compute their least generalization. An interesting question is whether the least ge...
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ISBN:
(数字)9783030974541
ISBN:
(纸本)9783030974541;9783030974534
This paper provides an empirical study for feature learning based on induction. We encode image data into first-order expressions and compute their least generalization. An interesting question is whether the least generalization can extract a common pattern of input data. We introduce three different methods for feature extraction based on symbolic manipulation. We perform experiments using the MNIst datasets and show that the proposed methods successfully capture features from training data and classify test data in around 90% accuracies. The results of this paper show potentials of induction and symbolic reasoning to feature learning or patternrecognition from raw data.
Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partial-defi...
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Automatic coin recognition and identification systemplays vital role in vending machine, slot machine and in several banking related equipment's. Most of the existing coin recognitions systems are based on physica...
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ISBN:
(纸本)9781538609262
Automatic coin recognition and identification systemplays vital role in vending machine, slot machine and in several banking related equipment's. Most of the existing coin recognitions systems are based on physical parameters of the coins whereas imageprocessing methodologies relies on extraction of color, shape and edge features. For recognition and detection of Indian coin we have proposed Deep learning approach in this paper. Pretrained convolutional neural network i.e. AlexNet is trained by using the features such as textures, colors and shape. The model is trained on more than 1600 images and can classify images into four object categories like one, two, five and ten rupees coins. The trained model is tested on various standard and own recorded datasets consist of rotational, translated and shifted images. The parameters used to calibrate the performance system are recognition accuracy and response time. Obtained results shown the outperformance of proposed methodology over conventional systems.
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