The PASCAL Visual Object Classes Challenge

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

Cell phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some relate to visual search capabilities, and determining appropriate actions responsive to different image inputs. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others relate to coping with fixed focus limitations of cell phone cameras, e.g., in reading digital watermark data. Still others concern user interface improvements. A great number of other features and arrangements are also detailed.

Methods and Systems for Content Processing

Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion1, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.

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DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

We present two novel methods for face verification. Our first method - “attribute” classifiers - uses binary classifiers trained to recognize the presence or absence of describable aspects of visual appearance (e.g., gender, race, and age). Our second method - “simile” classifiers - removes the manual labeling required for attribute classification and instead learns the similarity of faces, or regions of faces, to specific reference people. Neither method requires costly, often brittle, alignment between image pairs; yet, both methods produce compact visual descriptions, and work on real-world images. Furthermore, both the attribute and simile classifiers improve on the current state-of-the-art for the LFW data set, reducing the error rates compared to the current best by 23.92% and 26.34%, respectively, and 31.68% when combined. For further testing across pose, illumination, and expression, we introduce a new data set - termed PubFig - of real-world images of public figures (celebrities and politicians) acquired from the internet. This data set is both larger (60,000 images) and deeper (300 images per individual) than existing data sets of its kind. Finally, we present an evaluation of human performance.

Attribute and simile classifiers for face verification

Speech activity detection (or endpointing) is an important processing step for applications such as speech recognition, language identification and speaker diarization. Both audio- and vision-based approaches have been used for this task in various settings, often tailored toward end applications. However, much of the prior work reports results in synthetic settings, on task-specific datasets, or on datasets that are not openly available. This makes it difficult to compare approaches and understand their strengths and weaknesses. In this paper, we describe a new dataset which we will release publicly containing densely labeled speech activity in YouTube videos, with the goal of creating a shared, available dataset for this task. The labels in the dataset annotate three different speech activity conditions: clean speech, speech co-occurring with music, and speech co-occurring with noise, which enable analysis of model performance in more challenging conditions based on the presence of overlapping noise. We report benchmark performance numbers on AVA-Speech using off-the-shelf, state-of-the-art audio and vision models that serve as a baseline to facilitate future research.

AVA-Speech: A Densely Labeled Dataset of Speech Activity in Movies

A ground truth based vanishing point detection algorithm

In a method and system for correcting redeye in a digital image of a human, a defect pair separation is measured The members of the defect pair are each redeye defects in the image Each defect has one or more pixels The defects are adjusted, that is, retained unchanged or reduced in size to provide adjusted defects The adjustment can follow growth of initial seed defects The adjustment is responsive to the defect pair separation and can include trimming of pixels beyond a size limit calculated using the defect pair separation Following the adjustment, the color of the adjusted defects is changed to reduce redeye

Correction of redeye defects in images of humans

A method of determining the vertical axis of an image, including the steps of: detecting a set of vanishing points related to the image; selecting a vanishing point based on a predetermined criteria; and identifying the vertical axis with the selected vanishing point.

Method of estimating image format and orientation based upon vanishing point location

A method for comparing a plurality of photographers by assessing the aesthetic quality of a set of digital images captured by each photographer comprising: providing a set of digital images captured by each of a plurality of photographers; using a processor to determine an aesthetic quality parameter for each digital image in each of the sets of digital images, wherein the aesthetic quality parameter is an estimate for the aesthetic quality of the digital image; determining an aesthetic quality distribution for each photographer responsive to the aesthetic quality parameters computed for each of the digital images in the photographer's set of digital images; and providing a comparison between the aesthetic quality distributions of the photographers.

Andrew C. Gallagher

Papers

AVA-Speech: A Densely Labeled Dataset of Speech Activity in Movies

A ground truth based vanishing point detection algorithm

Correction of redeye defects in images of humans

Method of estimating image format and orientation based upon vanishing point location

Method for comparing photographer aesthetic quality