Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning

Due to the flexible representation of arbitrary-shaped scene text and simple pipeline, bottom-up segmentation-based methods begin to be mainstream in real-time scene text detection. Despite great progress, these methods show deficiencies in robustness and still suffer from false positives and instance adhesion. Different from existing methods which integrate multiple-granularity features or multiple outputs, we resort to the perspective of representation learning in which auxiliary tasks are utilized to enable the encoder to jointly learn robust features with the main task of per-pixel classification during optimization. For semantic representation learning, we propose global-dense semantic contrast (GDSC), in which a vector is extracted for global semantic representation, then used to perform element-wise contrast with the dense grid features. To learn instance-aware representation, we propose to combine top-down modeling (TDM) with the bottom-up framework to provide implicit instance-level clues for the encoder. With the proposed GDSC and TDM, the encoder network learns stronger representation without introducing any parameters and computations during inference. Equipped with a very light decoder, the detector can achieve more robust real-time scene text detection. Experimental results on four public datasets show that the proposed method can outperform or be comparable to the state-of-the-art on both accuracy and speed. Specifically, the proposed method achieves 87.2% F-measure with 48.2 FPS on Total-Text and 89.6% F-measure with 36.9 FPS on MSRA-TD500 on a single GeForce RTX 2080 Ti GPU.Comment: Accepted by ACM MM 202

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts. The simple linear iterative clustering method, which is based on superpixel theory, is then used to generate a patch from the selected images. After manually labeling each patch, they are divided into two categories: wheat ears and background. The color feature “Color Coherence Vectors,” the texture feature “Gray Level Co-Occurrence Matrix,” and a special image feature “Edge Histogram Descriptor” are then exacted from these patches to generate a high-dimensional matrix called the “feature matrix.” Because each feature plays a different role in the classification process, a feature-weighting fusion based on kernel principal component analysis is used to redistribute the feature weights. Finally, a twin-support-vector-machine segmentation (TWSVM-Seg) model is trained to understand the differences between the two types of patches through the features, and the TWSVM-Seg model finally achieves the correct classification of each pixel from the testing sample and outputs the results in the form of binary image. This process thus segments the image. Next, we use a statistical function in Matlab to get the exact a precise number of ears. To verify these statistical numerical results, we compare them with field measurements of the wheat plots. The result of applying the proposed algorithm to ground-shooting image data sets correlates strongly (with a precision of 0.79–0.82) with the data obtained by manual counting. An average running time of 0.1 s is required to successfully extract the correct number of ears from the background, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on wheat seedlings

HPV genotyping of cervical histologic specimens of 61, 422 patients from the largest women hospital in China

ObjectivesWe investigated HPV genotypes in a large cohort of patients with definitive cervical histologic diagnosis.MethodsHPV testing was performed by real-time PCR assay, including 18 high-risk HPV (hrHPV) and 3 low-risk HPV (lrHPV). Totally 61,422 patients with documented HPV genotyping results within 6 months before cervical histologic diagnoses were included.ResultsHrHPV positive rate was 55.1% among all tested cases with the highest in adenosquamous carcinoma (94.1%), followed by squamous cell carcinoma (SCC) (93.7%), cervical intraepithelial neoplasia 2/3 (CIN2/3) (92.8%). HrHPV positive rates were significantly higher in high-grade squamous lesions than in those in glandular lesions. HPV16 was the most common genotype followed by HPV52 and HPV58 in CIN2/3. The most frequent hrHPV genotype in adenocarcinoma in situ (AIS) was HPV18, followed by HPV16, HPV45 and HPV52. In SCC cases, HPV16 was the most common type followed by HPV58, HPV52, HPV18 and HPV33. However, HPV18 showed significantly higher prevalence in adenocarcinoma and adenosquamous carcinoma than in that in SCC. Theoretically, the protective rates of 2/4-valent and 9-valent vaccine were 69.1% and 85.8% for cervical cancers.ConclusionsThe prevalence of HPV genotypes in Chinese population was different from that in Western population. Some hrHPV types were identified in cervical precancerous lesions and cancers, which are not included in current HPV vaccines. These data provide baseline knowledge for future HPV vaccine development

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP) is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram–Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT), and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper

CHAOTIC SPREAD-SPECTRUM SEQUENCE GENERATED BY MULTILEVEL QUANTIFYING AND THEIR PROPERTIES

International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, CaliforniaAccording to the advantages of chaotic analog sequences and chaotic binary sequences, this paper proposes a method generating chaotic binary spread-spectrum sequence by multilevel quantifying. This paper proved that even correlation and odd correlation between such sequences of length N are all Gaussian distributed with mean 0 and variance N, the even of mean-square cross-correlation is N, and the variance of mean-square cross-correlation is 2N. The method can increase the number of chaotic sequences, made the spread-spectrum system more secure. The theoretical analyses and the results of simulation show that the performance of such sequence general is as same as traditional spread-spectrum sequence, its number is very large, and can be used in CDMA in future.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection

Improvement of Wheat Growth Information by Fusing UAV Visible and Thermal Infrared Images

To improve and enrich the wheat growth information, visible and thermal infrared (TIR) remote sensing images were simultaneously acquired by an unmanned aerial vehicle (UAV). A novel algorithm was proposed for fusing the visible and TIR images combing the intensity hue saturation (IHS) transform and regional variance matching (RVM). After registering the two images, IHS transform was first conducted to derive the Intensities of two images. Wavelet transform was then applied to the Intensities for obtaining the coefficients of low- and high-frequency sub-bands. The fusion rules of the fused image were developed based on regional correlation of wavelet decomposition coefficients. More specifically, the coefficients of low-frequency sub-bands were calculated by averaging the coefficients of two images. Regional variance was used to generate the coefficients of high-frequency sub-bands using the weighted template of a 3 × 3 pixel window. The inverse wavelet transform was used to create the new Intensity for the fused image using the low- and high-frequency coefficients. Finally, the inverse IHS transform consisting of the new Intensity, the Hue of visible image, and the Saturation of TIR image was adopted to change the IHS space to red–green–blue (RGB) color space. The fusion effects were validated by the visible and TIR images of winter wheat at the jointing stage and the middle and late grain-filling stage. Meanwhile, IHS and RV were also comparatively evaluated for validating our proposed method. The proposed algorithm can fully consider the correlation of wavelet coefficients in local regions. It overcomes the shortcomings (e.g., block phenomenon, color distortion) of traditional image fusion methods to obtain smooth, detailed and high-resolution images

Generating Spread-Spectrum Sequences by a Class of Chaotic Maps

International Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, NevadaBased on the fact that two topological conjugacy chaotic maps have identical dynamical behaviors, this paper proposes a method generating spreadspectrum sequences by creating chaotic maps topological conjugacy to Kent- Map, and analyses the correlation properties of the chaotic spread-spectrum sequences. The results of simulation verified the correctness of the theoretical analysis.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection

Improvement of Wheat Growth Information by Fusing UAV Visible and Thermal Infrared Images

To improve and enrich the wheat growth information, visible and thermal infrared (TIR) remote sensing images were simultaneously acquired by an unmanned aerial vehicle (UAV). A novel algorithm was proposed for fusing the visible and TIR images combing the intensity hue saturation (IHS) transform and regional variance matching (RVM). After registering the two images, IHS transform was first conducted to derive the Intensities of two images. Wavelet transform was then applied to the Intensities for obtaining the coefficients of low- and high-frequency sub-bands. The fusion rules of the fused image were developed based on regional correlation of wavelet decomposition coefficients. More specifically, the coefficients of low-frequency sub-bands were calculated by averaging the coefficients of two images. Regional variance was used to generate the coefficients of high-frequency sub-bands using the weighted template of a 3 × 3 pixel window. The inverse wavelet transform was used to create the new Intensity for the fused image using the low- and high-frequency coefficients. Finally, the inverse IHS transform consisting of the new Intensity, the Hue of visible image, and the Saturation of TIR image was adopted to change the IHS space to red–green–blue (RGB) color space. The fusion effects were validated by the visible and TIR images of winter wheat at the jointing stage and the middle and late grain-filling stage. Meanwhile, IHS and RV were also comparatively evaluated for validating our proposed method. The proposed algorithm can fully consider the correlation of wavelet coefficients in local regions. It overcomes the shortcomings (e.g., block phenomenon, color distortion) of traditional image fusion methods to obtain smooth, detailed and high-resolution images