다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogram robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.

/pdf/image-indexing-using-color-correlograms-46xcwi22a8.pdf

Image indexing using color correlograms

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.

https://link.springer.com/content/pdf/10.1007/s10462-020-09825-6.pdf

A survey of the recent architectures of deep convolutional neural networks

In this paper, we summarize the fundamental properties and review the latest developments in high power fiber lasers. The review is focused primarily on the most common fiber laser configurations and the associated cladding pumping issues. Special attention is placed on pump combination techniques and the parameters that affect the brightness enhancement observed in single-mode and multimode high power fiber lasers. The review includes the major limitations imposed by fiber nonlinearities and other parasitic effects, such as optical damage, transverse modal instabilities and photodarkening. Finally, the paper summarizes the power evolution in continuous-wave and pulsed ytterbium-doped fiber lasers and their impact on industrial applications.

High Power Fiber Lasers: A Review

A digital image of the object is captured and the object is recognized from plurality of objects in a database. An information address corresponding to the object is then used to access information and initiate communication pertinent to the object.

Image Capture and Identification System and Process

Handwritten digit recognition: benchmarking of state-of-the-art techniques

Handwritten digit recognition: investigation of normalization and feature extraction techniques

In character string recognition integrating segmentation and classification, high classification accuracy and resistance to noncharacters are desired to the underlying classifier. In a previous evaluation study, the modified quadratic discriminant function (MQDF) proposed by Kimura et al. was shown to be superior in noncharacter resistance but inferior in classification accuracy to neural networks. This paper proposes a discriminative learning algorithm to optimize the parameters of MQDF with aim to improve the classification accuracy while preserving the superior noncharacter resistance. We refer to the resulting classifier as discriminative learning QDF (DLQDF). The parameters of DLQDF adhere to the structure of MQDF under the Gaussian density assumption and are optimized under the minimum classification error (MCE) criterion. The promise of DLQDF is justified in handwritten digit recognition and numeral string recognition, where the performance of DLQDF is comparable to or superior to that of neural classifiers. The results are also competitive to the best ones reported in the literature.

Discriminative learning quadratic discriminant function for handwriting recognition

A camera (10) captures a subject facial image. A unit (30) generates a tracking signal containing feature extraction data. In conjunction with a memory store (40), the unit (30) generates an output signal representing a substitute face having the features of the subject face. To generate the tracking signal, the facial area is detected (3103, 3107-8) initially and used in location of the mouth (3102, 3110-3114) and eyes (3104-6, 3115-3118). Only the H and S components are used for detecting the facial area; and only the S and V components for the mouth within the facial area. A face vector may be generated (50) using the tracking signals.

Facial image processing

This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a statistical classifier (modified quadratic discriminant function, MQDF), three neural classifiers, and an LVQ (learning vector quantization) classifier. They are efficient in that high accuracies can be achieved at moderate memory space and computation cost. The performance is measured in terms of classification accuracy, sensitivity to training sample size, ambiguity rejection, and outlier resistance. The outlier resistance of neural classifiers is enhanced by training with synthesized outlier data. The classifiers are tested on a large data set extracted from NIST SD19. As results, the test accuracies of the evaluated classifiers are comparable to or higher than those of the nearest neighbor (1-NN) rule and regularized discriminant analysis (RDA). It is shown that neural classifiers are more susceptible to small sample size than MQDF, although they yield higher accuracies on large sample size. As a neural classifier, the polynomial classifier (PC) gives the highest accuracy and performs best in ambiguity rejection. On the other hand, MQDF is superior in outlier rejection even though it is not trained with outlier data. The results indicate that pattern classifiers have complementary advantages and they should be appropriately combined to achieve higher performance.

/pdf/performance-evaluation-of-pattern-classifiers-for-235ta1wsln.pdf

Hiroshi Sako

Papers

Handwritten digit recognition: benchmarking of state-of-the-art techniques

Handwritten digit recognition: investigation of normalization and feature extraction techniques

Discriminative learning quadratic discriminant function for handwriting recognition

Facial image processing

Performance evaluation of pattern classifiers for handwritten character recognition