Scale Selective Extended Local Binary Pattern for Texture Classification

In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multi-scale extended local binary patterns (ELBP) with rotation-invariant and uniform mappings to capture robust local micro- and macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale. Finally, we select the maximum values from the corresponding bins of multi-scale ELBP histograms at different scales as scale-invariant features. A comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows that the proposed SSELBP has high accuracy comparable to state-of-the-art texture descriptors on gray-scale-, rotation-, and scale-invariant texture classification but uses only one-third of the feature dimension.Comment: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 201

Event-based Asynchronous Sparse Convolutional Networks

Event cameras are bio-inspired sensors that respond to per-pixel brightness changes in the form of asynchronous and sparse "events". Recently, pattern recognition algorithms, such as learning-based methods, have made significant progress with event cameras by converting events into synchronous dense, image-like representations and applying traditional machine learning methods developed for standard cameras. However, these approaches discard the spatial and temporal sparsity inherent in event data at the cost of higher computational complexity and latency. In this work, we present a general framework for converting models trained on synchronous image-like event representations into asynchronous models with identical output, thus directly leveraging the intrinsic asynchronous and sparse nature of the event data. We show both theoretically and experimentally that this drastically reduces the computational complexity and latency of high-capacity, synchronous neural networks without sacrificing accuracy. In addition, our framework has several desirable characteristics: (i) it exploits spatio-temporal sparsity of events explicitly, (ii) it is agnostic to the event representation, network architecture, and task, and (iii) it does not require any train-time change, since it is compatible with the standard neural networks' training process. We thoroughly validate the proposed framework on two computer vision tasks: object detection and object recognition. In these tasks, we reduce the computational complexity up to 20 times with respect to high-latency neural networks. At the same time, we outperform state-of-the-art asynchronous approaches up to 24% in prediction accuracy

Portable Camera Based Assistive Pattern Recognition for Visually Challenged Persons

Choosing clothes, food recognition and traffic signal analysis are major challenges for visually impaired persons. The existing automatic clothing pattern recognition is also a challenging research problem due to rotation, scaling, illumination, and especially large intra class pattern variations. This project, a camera based assistive framework is proposed to help blind persons for identification of food pattern, clothe pattern and colors in their daily lives. The existing traffic signal using sensors method is difficult to analysis and many components used. A camera based traffic signal analysis method easy to handle, to provide clear traffic signal analysis and reduce the time delay. The system contains the following major components 1) a camera for capturing clothe, food and traffic signal images, a microphone for speech command input; 2) data capture and analysis to perform command control, recognize clothe patterns, food patterns and traffic signal identification by using a wearable computer and 3) a speaker to provide the name of audio outputs of clothe patterns and colors, food patterns and traffic signal analysis, as well as system status. To handle the large intra class variations, a novel descriptor, Radon Signature is proposed to capture the global directionality of clothe patterns, food patterns and traffic signal analysis. To evaluate the effectiveness of the proposed approach CCNY clothes Pattern dataset is used. Our approach achieves 92.55% recognition to improve the life quality, do not depend others. DOI: 10.17762/ijritcc2321-8169.15032

Detection of Counterfeit by the Usage of Product Inherent Features

AbstractOne aspect of the economical dimension of sustainable business development is the protection of high value products from counterfeiting. This holds especially true for consumer goods since the sustainable manufacturing process gains a more and more important role, e.g. in the creation of a brand image. In this paper we propose a method for detecting counterfeit by capture of inherent features indissolubly linked with the product induced by the production process itself. Since a counterfeiter gains margin by the use of inferior production processes and material the differences between genuine product and counterfeit can be captured in an automated fashion. The proposed method not only renders the application of artificial security tags obsolete which helps reducing the material usage but also gives enhanced protection against counterfeiting as the inherent characteristics cannot be removed from the article

Application of fractal analysis methods to images obtained by crystallization modified by an additive

The fractal and multifractal methods are now widely used for analysis and classification of digital images having complex structure. We present the results of the application of such methods to the images of crystallograms obtained by crystallization with additives. This technique was developed for studying images of blood crystals, and now finds increasing use in the analysis of medicines, checking food and soil quality. In this work we study images of crystallograms of various milk dilutions and crystallograms obtained with bean leave extracts. The results show that the proposed mathematical methods seem to be rather perspective both in comparing images of different classes and in obtaining classifying signs