PhosopNet: An improved grain localization and classification by image augmentation

Rice is a staple food for around 3.5 billion people in eastern, southern and south-east Asia. Prior to being rice, the rice-grain (grain) is previously husked and/or milled by the milling machine. Relevantly, the grain quality depends on its pureness of particular grain specie (without the mixing between different grain species). For the demand of grain purity inspection by an image, many researchers have proposed the grain classification (sometimes with localization) methods based on convolutional neural network (CNN). However, those papers are necessary to have a large number of labeling that was too expensive to be manually collected. In this paper, the image augmentation (rotation, brightness adjustment and horizontal flipping) is appiled to generate more number of grain images from the less data. From the results, image augmentation improves the performance in CNN and bag-of-words model. For the future moving forward, the grain recognition can be easily done by less number of images

Neural network in rice grading: How Malaysian rice can be graded?

Rice grading plays an important role in the determination of rice quality and its subsequent price in the market.It is an important process applied in the rice production industry with the purpose ensuring that the rice produced for the market meets the quality requirements of consumer. Two important aspects that need to be considered in determining rice grades; grading technique and factors to be used for grading (usually referred as rice attributes).This article proposes how Malaysian rice can be graded. Twenty one features are proposed to be used.Combination of extensive literature review and series of interview were used in determining the features. A Neural Network (NN) model is proposed to be used with the identified features.For evaluation purpose, expert review has been carried out.The proposed model is believed to be beneficial not only for BERNAS but also to other researchers in the same domain. BERNAS can use the NN model to facilitate their inspection for rice quality.The model can be used as guidance or reference for similar grading works

Entropic Origin of Pseudogap Physics and a Mott-Slater Transition in Cuprates

We propose a new approach to understand the origin of the pseudogap in the cuprates, in terms of bosonic entropy. The near-simultaneous softening of a large number of different $q$-bosons yields an extended range of short-range order, wherein the growth of magnetic correlations with decreasing temperature $T$ is anomalously slow. These entropic effects cause the spectral weight associated with the Van Hove singularity (VHS) to shift rapidly and nearly linearly toward half filling at higher $T$, consistent with a picture of the VHS driving the pseudogap transition at a temperature $\sim T^*$. As a byproduct, we develop an order-parameter classification scheme that predicts supertransitions between families of order parameters. As one example, we find that by tuning the hopping parameters, it is possible to drive the cuprates across a {\it transition between Mott and Slater physics}, where a spin-frustrated state emerges at the crossover.Comment: 24 pgs, 15 figs + Supp. Material [6pgs, 3 figs]. Major revision of arXiv:1505.0477

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%