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

A method of improving a digital color image having pixels, the method includes identifying pixels from the digital color image representing one or more sky regions, detecting sky occlusion regions by examining the sky regions, developing a model based on the identified sky pixels, wherein such model is a mathematical function that has inputs of pixel position and outputs of color, and using the model to operate on the digital color image to replace the values of pixels from the sky occlusion regions with values predicted by the model.

Detecting sky occlusion objects in color digital images

A method for indicating an incoming phone call in a mobile phone, comprising: - storing personal calendar information in the mobile phone, the personal calendar information including occasion or appointment information and information indicating that at least one occasion or appointment is a silent event; - detecting an incoming phone call from a mobile phone network ; - determining a current date and time; - comparing the current date and time with the personal calendar information to determine whether the current date and time corresponds to the silent event; - if the current date and time corresponds to the silent event, providing a non-audible indication of the incoming call; and - if the current data and time does not correspond to the silent event, providing an audible indication of the incoming call

Controlling the incoming call indication function of a mobile phone

Implementations relate to an automated assistant that provides and manages output from one or more elements of output hardware of a computing device. The automated assistant manages dynamic adjustment of access permissions to the computing device according to, for example, a detected presence of one or more users. An active-user queue can be established each time a unique user enters a viewing window of a camera of the computing device when, up to that point, no user was considered active. Multiple image frames can be captured via the camera and processed to determine whether an initial user remains in the viewing window and/or whether another user has entered the viewing window. The initial user can be considered active as long as they are exclusively detected in the viewing window. Restricted content associated with the user may be rendered by the computing device whilst the user is active.

Andrew C. Gallagher

Papers

Detecting sky occlusion objects in color digital images

Controlling the incoming call indication function of a mobile phone

Assigning priority for an automated assistant according to a dynamic user queue and/or multi-modality presence detection