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.

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.

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

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.

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 signalto-noise ratio data from all experimental data and then to use these selected data for elliptical imaging. The 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.

Color image single-channel encryption based on tricolor grating theory

A method of color image single-channel encryption is proposed. The proposed method uses tricolor grating to encode a color image into a gray level image, then the gray level image is encrypted by double random phase encryption, so a color image is encrypted in a single-channel and its security is ensured. Computer simulations and the chromatic aberration analysis are given to prove the possibility of the proposed idea.The optical system is simpler and is easy to be applied into practice. The simulation results show that this method is efficiency to encrypt a color image, and it is robust.

Text extraction method for historical Tibetan document images based on block projections

Text extraction is an important initial step in digitizing the historical documents. In this paper, we present a text extraction method for historical Tibetan document images based on block projections. The task of text extraction is considered as text area detection and location problem. The images are divided equally into blocks and the blocks are filtered by the information of the categories of connected components and corner point density. By analyzing the filtered blocks' projections, the approximate text areas can be located, and the text regions are extracted. Experiments on the dataset of historical Tibetan documents demonstrate the effectiveness of the proposed method.

Global point tracking based panoramic image stabilization system

A novel image stabilization system is presented,which consists of a global feature point tracking based motion estimation,a Kalman filtering based motion smoothing and an image mosaic based panoramic compensation.The global motion is estimated using feature point matching and iteration with the least-square method.Then,the Kalman filter is applied to smooth the original motion vectors to effectively alleviate unwanted camera vibrations and follow the intentional camera scan.Lastly,the loss information of image boundary due to the motion compensation is reconstructed with image mosaic to improve the visual quality.The experimental results show that this system can smooth unwanted translation or rotation of the video sequences and realize a panoramic stabilization at real-time speed.

An adaptive interpolation algorithm for hole-filling in free viewpoint video

Depth image based rendering(DIBR) is an effective approach for virtual view synthesis in free viewpoint television and 3D video.One of the important steps in DIBR is filling the holes caused by disocclusion regions and wrong depth values.Most of the existing hole-filling methods work well in areas of low spatial activity but fail to obtain satisfactory results in high spatial activity regions.In this paper,we combine the depth based hole-filling and the adaptive recursive interpolation algorithm which is capable of handling edges passing through the missing areas.Accoring to the experimental results,we confirm that the depth based adaptive recursive interpolation algorithm can provide better rendering quality objectively and subjectively.

An effective graph and depth layer based RGB-D image foreground object extraction method

We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths of contents for display purposes in 3DTV,object detection,or scene understanding.To

石窟寺数据库建设中Image Based Modeling照片阵列建模的应用探索——以桂林石窟寺调查为例

石窟寺是文物的重要组成部分,本文以2021年全国石窟寺专项调查工作为基础,讨论了Image Based Modeling建模技术的相关发展以及应用,探寻其应用于石窟寺调查的可能性,及其应用于日后文物保护研究以及建设文物数据库的重要价值。

Cross-talk-free integral imaging three-dimensional display based on a pyramid pinhole array

We propose a cross-talk-free integral imaging 3D display based on a pyramid pinhole array. The pyramid pinhole array is used to provide a point light source array. Since the generated point light source array is behind a transmission-type display panel that displays an elemental image array, the pseudoscopic problem can be resolved. By setting the appropriate parameters for the pyramid pinhole array, the cross talk can be eliminated.We experimentally verify the reconstruction of the orthoscopic and cross-talk-free 3D images using the proposed 3D display.

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.

A lattice measuring method based on integral imaging technology

Aiming at the problem that the lattice feature exceeds the view field of the scanning electron microscope(SEM)measuring system,a new lattice measuring method is proposed based on integral imaging technology.When the system works,the SEM measuring system is equivalent to an integral image acquisition system.Firstly,a lattice measuring method is researched based on integral imaging theory.Secondly,the system parameters are calibrated by the VLSI lattice standard.Finally,the value of the lattice standard to be tested is determined based on the calibration parameters and the lattice measuring algorithm.The experimental results show that,compared with the traditional electron microscope measurement method,the relative error of the measured value of the algorithm is maintained within 0.2%,with the same level of measurement accuracy,but it expands the field of view of the electron microscope measurement system,which is suitable for the measurement of samples under high magnification.

Three-dimensional measurement of bubble volume based on dual perspective imaging

This paper presents a new three-dimensional(3D) volume measurement approach of bubble in gas-liquid two-phase flow. According to the dual perspective imaging principle,bubble feature images can be captured from two different view angles. The least square ellipse fitting algorithm is used to figure out the feature parameters from the captured images. Then the 3D volume of bubble can be quantitatively measured. Compaerd with the traditional volume estimation methods based on single perspective imaging,it can effectively reduce the loss of bubble feature information. In the experiment,the 3D volume reconstruction of bubbles from dual perspective images is conducted,and the variation of bubble volume in the bubble rising process is studied. The results show that the measurement accuracy based on the proposed 3D method is higher than those based on traditional methods. The volume of rising bubble is periodically changed,which indicates that bubble achieves periodic rotation and deformation in the rising process.

Bimorph deformable mirror based adaptive optics scanning laser ophthalmoscope for retina imaging in vivo

A bimorph deformable mirror(DM) with a large stroke of more than 30 μm using 35 actuators is presented and characterized for an adaptive optics(AO) confocal scanning laser ophthalmoscope application. Facilitated with a Shack–Hartmann wavefront sensor, the bimorph DM-based AO operates closed-loop AO corrections for human eyes and reduces wavefront aberrations in most eyes to below 0.1 μm rms. Results from living eyes, including one exhibiting ～5D of myopia and ～2D of astigmatism along with notable high-order aberrations, reveal a practical efficient aberration correction and demonstrate a great benefit for retina imaging, including improving resolution, increasing brightness, and enhancing the contrast of images.

Nonorthogonal object identification based on ghost imaging

Ghost imaging could be used to make a quick identification of orthogonal objects by means of photocurrent correlation measurements. In this paper, we extend the method to identify nonorthogonal objects. In the method, an object is illuminated by one photon from an entangled pair, and the other one is diffracted into a particular direction by a pre-established multiple-exposure hologram in the idler arm. By the correlation measurements, the nonorthogonal object in the signal arm could be discriminated within a very short time. The constraints for the identification of nonorthogonal objects are presented, which show that the nonorthogonal objects can be discriminated when the overlapping portion between any two objects is less than half of all the objects in the set. The numerical simulations further verify the result.