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

The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.

This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

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The Pascal Visual Object Classes (VOC) Challenge

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sharing of such annotations. Using this annotation tool, we have collected a large dataset that spans many object categories, often containing multiple instances over a wide variety of images. We quantify the contents of the dataset and compare against existing state of the art datasets used for object recognition and detection. Also, we show how to extend the dataset to automatically enhance object labels with WordNet, discover object parts, recover a depth ordering of objects in a scene, and increase the number of labels using minimal user supervision and images from the web.

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LabelMe: A Database and Web-Based Tool for Image Annotation

Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We explore the use of Amazon's Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web. We investigate five tasks: affect recognition, word similarity, recognizing textual entailment, event temporal ordering, and word sense disambiguation. For all five, we show high agreement between Mechanical Turk non-expert annotations and existing gold standard labels provided by expert labelers. For the task of affect recognition, we also show that using non-expert labels for training machine learning algorithms can be as effective as using gold standard annotations from experts. We propose a technique for bias correction that significantly improves annotation quality on two tasks. We conclude that many large labeling tasks can be effectively designed and carried out in this method at a fraction of the usual expense.

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Cheap and Fast -- But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks

A generic algorithm is presented for automatic extraction of buildings and roads from complex urban environments in high-resolution satellite images where the extraction of both object types at the same time enhances the performance. The proposed approach exploits spectral properties in conjunction with spatial properties, both of which actually provide complementary information to each other. First, a high-resolution pansharpened colour image is obtained by merging the high-resolution panchromatic (PAN) and the low-resolution multispectral images yielding a colour image at the resolution of the PAN band. Natural and man-made regions are classified and segmented by the Normalized Difference Vegetation Index (NDVI). Shadow regions are detected by the chromaticity to intensity ratio in the YIQ colour space. After the classification of the vegetation and the shadow areas, the rest of the image consists of man-made areas only. The man-made areas are partitioned by mean shift segmentation where some resulting ...

Unsupervised building detection in complex urban environments from multispectral satellite imagery

Visual and infrared cameras have complementary properties and using them together may increase the performance of object detection applications. Although the fusion of visual and infrared information results in a better recall rate than using only one of those domains, there is always a decrease in the precision rate whereas the infrared domain on its own always has higher precision. Thus, the fusion of these domains is meaningful only for a better recall rate, which means that more foreground pixels are detected correctly. This study presents a new computationally more efficient and simpler method for extracting the complementary information from both domains and fusing them to obtain better recall rates than those previously achieved. The method has been tested using a well-known database and a database created for the study and compared with earlier fusion methods.

New method for the fusion of complementary information from infrared and visual images for object detection

Many approaches to object recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the distribution of the image features is modelled. Generative and discriminative methods have very different characteristics, as well as complementary strengths and weaknesses. In this chapter we introduce new generative and discriminative models for object detection and classification based on weakly labelled training data. We use these models to illustrate the relative merits of the two approaches in the context of a data set of widely varying images of non-rigid objects (animals). Our results support the assertion that neither approach alone will be sufficient for large scale object recognition, and we discuss techniques for combining the strengths of generative and discriminative approaches.

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Comparison of Generative and Discriminative Techniques for Object Detection and Classification

An algorithm is proposed for 3D object representation using generic 3D features which are transformation and scale invariant. Descriptive 3D features and their relations are used to construct a graphical model for the object which is later trained and then used for detection purposes. Descriptive 3D features are the fundamental structures which are extracted from the surface of the 3D scanner output. This surface is described by mean and Gaussian curvature values at every data point at various scales and a scale-space search is performed in order to extract the fundamental structures and to estimate the location and the scale of each fundamental structure.

3D Object Representation Using Transform and Scale Invariant 3D Features

The recent success of deep learning is mostly due to the availability of big datasets with clean annotations. However, gathering a cleanly annotated dataset is not always feasible due to practical challenges. As a result, label noise is a common problem in datasets, and numerous methods to train deep neural networks in the presence of noisy labels are proposed in the literature. These methods commonly use benchmark datasets with synthetic label noise on the training set. However, there are multiple types of label noise, and each of them has its own characteristic impact on learning. Since each work generates a different kind of label noise, it is problematic to test and compare those algorithms in the literature fairly. In this work, we provide a detailed analysis of the effects of different kinds of label noise on learning. Moreover, we propose a generic framework to generate feature-dependent label noise, which we show to be the most challenging case for learning. Our proposed method aims to emphasize similarities among data instances by sparsely distributing them in the feature domain. By this approach, samples that are more likely to be mislabeled are detected from their softmax probabilities, and their labels are flipped to the corresponding class. The proposed method can be applied to any clean dataset to synthesize feature-dependent noisy labels. For the ease of other researchers to test their algorithms with noisy labels, we share corrupted labels for the most commonly used benchmark datasets. Our code and generated noisy synthetic labels are available online.

Ilkay Ulusoy

Papers

Unsupervised building detection in complex urban environments from multispectral satellite imagery

New method for the fusion of complementary information from infrared and visual images for object detection

Comparison of Generative and Discriminative Techniques for Object Detection and Classification

3D Object Representation Using Transform and Scale Invariant 3D Features

Label Noise Types and Their Effects on Deep Learning