There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

Deep learning models for plant disease detection and diagnosis

Deep Learning has solved complicated applications with increasing accuracies over time. The recent interest in this technology, especially in its potential application in agriculture, has powered the growth of efficient systems to solve real problems, such as non-destructive methods for plant anomalies recognition. Despite the advances in the area, there remains a lack of performance in real-field scenarios. To deal with those issues, our research proposes an efficient solution that provides farmers with a technology that facilitates proper management of crops. We present two efficient techniques based on deep learning for plant disease recognition. The first method introduces a practical solution based on a deep meta-architecture and a feature extractor to recognize plant diseases and their location in the image. The second method addresses the problem of class imbalance and false positives through the introduction of a refinement function called Filter Bank. We validate the performance of our methods on our tomato plant diseases and pest dataset. We collected our own data and designed the annotation process. Qualitative and quantitative results show that despite the complexity of real-field scenarios, plant diseases are successfully recognized. The insights drawn from our research helps to better understand the strengths and limitations of plant diseases recognition.

Deep Learning-Based Techniques for Plant Diseases Recognition in Real-Field Scenarios

Most methods for fusion-based finger vein recognition were to fuse different features or matching scores from more than one trait to improve performance To overcome the shortcomings of “the curse of dimensionality” and additional running time in feature extraction, in this paper, we propose a finger vein recognition technology based on matching score-level fusion of a single trait To enhance the quality of finger vein image, the contrast-limited adaptive histogram equalization (CLAHE) method is utilized and it improves the local contrast of normalized image after ROI detection Gabor features are then extracted from eight channels based on a bank of Gabor filters Instead of using the features for the recognition directly, we analyze the contributions of Gabor feature from each channel and apply a weighted matching score-level fusion rule to get the final matching score, which will be used for the last recognition Experimental results demonstrate the CLAHE method is effective to enhance the finger vein image quality and the proposed matching score-level fusion shows better recognition performance

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Finger Vein Recognition based on Matching Score-Level Fusion of Gabor Features

Deep learning shows its advantages and potentials in plant disease recognition and has witnessed a profound development in recent years. To obtain a competing performance with a deep learning algorithm, enough amount of annotated data is requested but in the natural world, scarce or imbalanced data are common, and annotated data is expensive or hard to collect. Data augmentation, aiming to create variations for training data, has shown its power for this issue. But there are still two challenges: creating more desirable variations for scarce and imbalanced data, and designing a data augmentation to ease object detection and instance segmentation. First, current algorithms made variations only inside one specific class, but more desirable variations can further promote performance. To address this issue, we propose a novel data augmentation paradigm that can adapt variations from one class to another. In the novel paradigm, an image in the source domain is translated into the target domain, while the variations unrelated to the domain are maintained. For example, an image with a healthy tomato leaf is translated into a powdery mildew image but the variations of the healthy leaf are maintained and transferred into the powdery mildew class, such as types of tomato leaf, sizes, and viewpoints. Second, current data augmentation is suitable to promote the image classification model but may not be appropriate to alleviate object detection and instance segmentation model, mainly because the necessary annotations can not be obtained. In this study, we leverage a prior mask as input to tell the area we are interested in and reuse the original annotations. In this way, our proposed algorithm can be utilized to do the three tasks simultaneously. Further, We collect 1,258 images of tomato leaves with 1,429 instance segmentation annotations as there is more than one instance in one single image, including five diseases and healthy leaves. Extensive experimental results on the collected images validate that our new data augmentation algorithm makes useful variations and contributes to improving performance for diverse deep learning-based methods.

/pdf/style-consistent-image-translation-a-novel-data-augmentation-1nc80dtx.pdf

Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition

Finger vein is a new and secure biometric for personal authentication due to its line-structure network with abundant local and orientation features. However, these features cannot be well represented by the existing local descriptors. To effectively utilize these rich orientation features in finger vein images, this paper proposes a new local descriptor, namely, pyramid histogram of double competitive pattern (PHDCP). For a finger vein image, the PHDCP first obtains a bank of filtered images using Gabor filters with large kernel size and rich orientations, by which the local line features are captured. Then, the orientation orders with the largest and smallest responses, which are the most robust features, are selected to generate an encoded map. Finally, a column-partition-based pyramid histogram extraction method is presented to capture the hierarchical features from the encoded image. Numerical experiments are conducted on two public finger vein data sets, MMCBNU_6000 and UTFVP. The experimental results demonstrate that the proposed PHDCP performs much better than the existing local descriptors.

Pyramid Histogram of Double Competitive Pattern for Finger Vein Recognition

Analyzing the quality of fingerprints in advance can be benefit for a fingerprint recognition system to improve its performance. Representative features for the quality assessment of fingerprint images from two existed types of capture devices are different. Orientation certainty level (OCL) is an effective method to extract image orientation feature. However it is not an effective estimation system to cooperate with the extracted features. In this paper, we explore the application of optimization theory, and support vector machine (SVM) in the field of image processing. Our proposed optimal orientation certainty level (OOCL) approach calculates the OCL for each block, extracts features from the optimal OCL system and uses the SVM classifier to determine whether an image should be accepted as an input to the recognition system. Experimental results show that the proposed OOCL method can improve the recognition rate than OCL method.

Dong Sun Park

Papers

Deep Learning-Based Techniques for Plant Diseases Recognition in Real-Field Scenarios

Finger Vein Recognition based on Matching Score-Level Fusion of Gabor Features

Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition

Pyramid Histogram of Double Competitive Pattern for Finger Vein Recognition

An Optimal Orientation Certainty Level Approach for Fingerprint Quality Estimation