Machine Vision Analysis of Characteristics and Image Information Base Construction for Hybrid Rice Seed

Incompletely closed glumes, germination on panicle and disease are three important factors causing poor seed quality of hybrid rice. To determine how many and which categories should be classified to meet the demand for seed in rice production, the effects of various degrees of incompletely closed glumes, germination on panicle and disease on germination percentage at the harvest and after storage for six months were studied by standard germination percentage test. Six categories of seeds with germ (germinated seeds), severe disease, incompletely closed glumes, spot disease, fine fissure and normal seeds were inspected and then treated separately. Images of the five hybrid rice seed (Jinyou 402, Shanyou 10, Zhongyou 27, Jiayou 99 and Ⅱ you 3207) were acquired with a self-developed machine vision system. Each image could be processed to get the feature values of seed region such as length, width, ratio of length to width, area, solidity and hue. Then all the images of normal seeds were calculated to draw the feature value ranges of each hybrid rice variety. Finally, an image information base that stores typical images and related feature values of each variety was established. This image information base can help us to identify the classification limit of characteristics, and provide the reference of the threshold selection. The management of large numbers of pictures and the addition of new varieties have been supported. The research laid a foundation for extracting image features of hybrid rice seed, which is a key approach to future quality inspection with machine vision.

Real-time digital image stabilization based on regional field image gray projection

Digital image stabilization technique plays important roles in video surveillance and object acquisition.Many useful electronic image stabilization algorithms have been studied.A real-time algorithm is proposed based on field image gray projection which enables the regional odd and even field image to be projected into x and y directions and thus to get the regional gray projection curves in x and y directions,respectively.For the odd field image channel,motion parameters can be estimated via iterative minimum absolute difference based on two successive field image regional gray projection curves.Then motion compensations can be obtained after using the Kalman filter method.Finally,the odd field image is adjusted according to the compensations.In the mean time,motion compensation is applied to the even field image channel with the odd field image gray projection curves of the current frame.By minimizing absolute difference between odd and even field image gray projection curves of the current frame,the inter-field motion parameters can be estimated.Therefore,the even field image can be adjusted by combining the inter-field motion parameters and the odd field compensations.Finally,the stabilized image sequence can be obtained by synthesizing the adjusted odd and even field images.Experimental results show that the proposed algorithm can run in real-time and have a good stabilization performance.In addition,image blurring can be improved.

An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.

2D matrix based indexing with color spectralhistogram for efficient image retrieval

A novel content based image retrieval （CBIR） algorithmusing relevant feedback is presented. The proposed frameworkhas three major contributions： a novel feature descriptor calledcolor spectral histogram （CSH） to measure the similarity betweenimages; two-dimensional matrix based indexing approach proposedfor short-term learning （STL）; and long-term learning （LTL）.In general, image similarities are measured from feature representationwhich includes color quantization, texture, color, shapeand edges. However, CSH can describe the image feature onlywith the histogram. Typically the image retrieval process starts byfinding the similarity between the query image and the imagesin the database; the major computation involved here is that theselection of top ranking images requires a sorting algorithm to beemployed at least with the lower bound of O（n log n）. A 2D matrixbased indexing of images can enormously reduce the searchtime in STL. The same structure is used for LTL with an aim toreduce the amount of log to be maintained. The performance ofthe proposed framework is analyzed and compared with the existingapproaches, the quantified results indicates that the proposedfeature descriptor is more effectual than the existing feature descriptorsthat were originally developed for CBIR. In terms of STL,the proposed 2D matrix based indexing minimizes the computationeffort for retrieving similar images and for LTL, the proposed algorithmtakes minimum log information than the existing approaches.

Construction of three-dimensional atlas of the lenticular nuclei and its subnucleus based on the cryosection images from Chinese visible human:a preliminary study

Objective To establish a 3D atlas of the lenticular nuclei and its subnucleus with the cryosection images of the male from "Atlas of Chinese Visible Human". Methods The lenticular nuclei and its subnucleus were segmented from the cryosection images and reconstructed with the software

Machine Vision Based Measurement of Dynamic Contact Angles in Microchannel Flows

When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and sealed with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB environment. Images are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.

Terahertz Three-Dimensional Imaging Based on Computed Tomography with Photonics-Based Noise Source

Computed tomography has been proven to be useful for non-destructive inspection of structures and materials. We build a three-dimensional imaging system with the photonically generated incoherent noise source and the Schottky barrier diode detector in the terahertz frequency band （90–140GHz）. Based on the computed tomography technique, the three-dimensional image of a ceramic sample is reconstructed successfully by stacking the slices at different heights. The imaging results not only indicate the ability of terahertz wave in the non-invasive sensing and non-destructive inspection applications, but also prove the effectiveness and superiority of the uni-traveling-carrier photodiode as a terahertz source in the imaging applications.

Attack on Optical Double Random Phase Encryption Based on the Principle of Ptychographical Imaging

The principle of ptychography is applied in known plain text attack on the double random phase encoding （DRPE） system. We find that with several pairs of plain texts and cipher texts, the model of attack on DRPE can be converted to the model of ptyehographical imaging. Owing to the inherent merits of the ptyehographical imaging, the DRPE system can be breached totally in a fast and nearly perfect way, which is unavailable for currently existing attack methods. Further, since the decryption keys can be seen as an object to be imaged from the perspective of imaging, the ptychographical technique may be a kind of new direction to further analysis of the security of other encryption systems based on double random keys.

View-invariant Gait Authentication Based on Silhouette Contours Analysis and View Estimation

In this paper, we propose a novel view-invariant gait authentication method based on silhouette contours analysis and view estimation. The approach extracts Lucas-Kanade based gait flow image and head and shoulder mean shape(LKGFI-HSMS)of a human by using the Lucas-Kanade s method and procrustes shape analysis(PSA). LKGFI-HSMS can preserve the dynamic and static features of a gait sequence. The view between a person and a camera is identified for selecting the target s gait feature to overcome view variations. The similarity scores of LKGFI and HSMS are calculated. The product rule combines the two similarity scores to further improve the discrimination power of extracted features. Experimental results demonstrate that the proposed approach is robust to view variations and has a high authentication rate.

Computational Spectral Imaging Based on Compressive Sensing

Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial infor- mation is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens. The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.

Wide-Field Vibrational Phase Contrast Imaging Based on Coherent Anti-Stokes Raman Scattering Holography

We propose and implement a wide-field vibrational phase contrast detection to obtain imaging of imaginary components of third-order nonlinear susceptibility in a coherent anti-Stokes Raman scattering （CARS） microscope with full suppression of the non-resonant background. This technique is based on the unique ability of recovering the phase of the generated CARS signal based on holographic recording. By capturing the phase distributions of the generated CARS field from the sample and from the environment under resonant illumination, we demonstrate the retrieval of imaginary components in the CARS microscope and achieve background free coherent Raman imaging.

Distance-Coefficient-Based Imaging Accuracy Improving Method Based on the Lamb Wave

An imaging accuracy improving method is established, within which a distance coefficient including location information between sparse array configuration and the location of defect is proposed to select higher signal- to-noise ratio data from all experimental data and then to use these selected data for elliptical imaging. Tile relationships among imaging accuracy, distance coefficient and residual direct wave are investigated, and then the residual direct wave is introduced to make the engineering application more convenient. The effectiveness of the proposed method is evaluated experimentally by sparse transducer array of a rectangle, and the results reveal that selecting experimental data of smaller distance coefficient can effectively improve imaging accuracy. Moreover, the direct wave difference increases with the decrease of the distance coefficient, which implies that the imaging accuracy can be effectively improved by using the experimental data of the larger direct wave difference.

Shift Invariance Level Comparison of Several Contourlet Transforms and Their Texture Image Retrieval Systems

In this paper, we proposed a metric to measure the shift invariance of the three different contourlet transforms. And then, using the same structure texture image retrieval system which use subband coefficients energy, standard deviation and kurtosis features with Canberra distance, we gave a comparison of their texture description abilities. Experimental results show that contourlet-2.3 texture image retrieval system has almost retrieval rates with non-sub sampled contourlet system;the two systems have better retrieval results than the original contourlet retrieval system. On the other hand, for the relatively lower redundancy, we recommend using contourlet- 2.3 as texture description transform.

Fabricating lifted Haar transform image compression optical chip based on femtosecond laser

In this paper,a lifted Haar transform(LHT)image compression optical chip has been researched to achieve rapid image compression.The chip comprises 32 same image compression optical circuits,and each circuit contains a 2×2 multimode interference(MMI)coupler and aπ/2 delay line phase shifter as the key components.The chip uses highly borosilicate glass as the substrate,Su8 negative photoresist as the core layer,and air as the cladding layer.Its horizontal and longitudinal dimensions are 8011μm×10000μm.Simulation results present that the designed optical circuit has a coupling ratio(CR)of 0:100 and an insertion loss(IL)of 0.001548 d B.Then the chip is fabricated by femtosecond laser and testing results illustrate that the chip has a CR of 6:94 and an IL of 0.518 d B.So,the prepared chip possesses good image compression performance.

An improved Bag-of-Words framework for remote sensing image retrieval in large-scale image databases

Due to advances in satellite and sensor technology,the number and size of Remote Sensing(RS)images continue to grow at a rapid pace.The continuous stream of sensor data from satellites poses major challenges for the retrieval of relevant information from those satellite datastreams.The Bag-of-Words(BoW)framework is a leading image search approach and has been successfully applied in a broad range of computer vision problems and hence has received much attention from the RS community.However,the recognition performance of a typical BoW framework becomes very poor when the framework is applied to application scenarios where the appearance and texture of images are very similar.In this paper,we propose a simple method to improve recognition performance of a typical BoW framework by representing images with local features extracted from base images.In addition,we propose a similarity measure for RS images by counting the number of same words assigned to images.We compare the performance of these methods with a typical BoW framework.Our experiments show that the proposed method has better recognition performance than that of the BoW and requires less storage space for saving local invariant features.

Evaluation of semivariogram features for objectbased image classification

Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divided into segments,for each of which a semivariogram is then calculated.Second,candidate features are extracted as a number of key locations of the semivariogram functions.Then we use an improved Relief algorithm and the principal component analysis to select independent and significant features.Then the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine classifier.The effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix(GLCM)features and window-based semivariogram texture features(STFs).Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral features.The described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.

1D integral imaging based on parallax images' virtual reconstruction

One-dimensional （ID） integral imaging based on parallax images＇ virtual reconstruction is proposed. The 1D integral imaging contains parallax images＇ capture process, parallax images＇ virtual reconstruction process, and ID elemental image array＇s generation process. A pixel mapping algorithm is deduced to implement the last two processes; a ID elemental image array is generated by the mapping of pixels on the parallax images obtained using a ID camera array. The proposed ID integral imaging can capture the ID elemental image array of a real three-dimensional （3D） scene.

An Image Skeletonization-Based Tool for Pollen Tube Morphology Analysis and Phenotyping

The mechanism underlying pollen tube growth involves diverse genes and molecular pathways. Alterations in the regulatory genes or pathways cause phenotypic changes reflected by cellular morphology, which can be captured using fluorescence microscopy. Determining and classifying pollen tube morphological phenotypes in such microscopic images is key to our understanding the involvement of genes and pathways. In this context, we propose a computational method to extract quantitative morphological features, and demonstrate that these features reflect morphological differences relevant to distinguish different defects of pollen tube growth. The corresponding software tool furthermore includes a novel semi-automated image segmentation approach, allowing to highly accurately identify the boundary of a pollen tube in a microscopic image.

A united optimum images fusion based on analysis of color distortion

In remote sensing community, IHS (intensity, hue, and saturation) transform is one of the most commonly used fusion algorithm. A study on IHS fusion indicates that the color distortion cannot be avoided. Meanwhile, wavelet decomposition has a property of frequency division in transform domain. And the statistical property of wavelet coefficient reflects those significant features. So, a united optimal fusion method, which using the statistical property of wavelet decomposition and IHS transform on pixel and