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

PROBLEM TO BE SOLVED: To provide an imaging device which does not have loss in small parts in space or spatial resolution loss, even in a state of excessive exposure. SOLUTION: The imaging device comprises a first type of element sensitive to a spectral region corresponding to luminance, a second type of element which is sensitive primarily to red light, a third type of element which is sensitive primarily to green light, and a fourth type of element which is sensitive primarily to blue light. An array of light-sensitive elements is structured so that the four types of elements occur in repeating patterns.

Sparsely sampled imaging device having color and luminance photosite

PROBLEM TO BE SOLVED: To prevent a defect near a large edge and to operate a tone scale function upon a digital image by preparing a corrected digital image channel by adding a digital image channel and a compensating signal. SOLUTION: A tone scale differentiator 30 receives the digital image channel from the digital image, controls the tone scale of that digital image channel, calculates a difference between a digital image channel changed by that tone scale function and the original digital image channel, prepares a differential signal and sends it to a frequency splitter 70. Corresponding to the differential signal, the tone splitter 70 prepares a low frequency signal, inputs this low frequency signal to an avoidance adder 90, adds the differential signal and a low frequency differential signal while weighing them and outputs the compensating signal to an adder 95. On the basis of the compensating signal and the original digital image channel, the adder 95 prepares the corrected digital image channel.

Method for controlling tone scale of digital image

An image processing system includes an image collector and a light falloff correction system. The light falloff correction system comprises a polar transformer that converts an image into radial traces and a falloff fitter that fits the radial traces to a model of falloff to determine a light falloff correction for the image.

A method of and system for automatically determining a level of light falloff in an image

When we see other humans, we can quickly make judgements regarding many aspects, including their demographic description and identity if they are familiar to us. We can answer questions related to the activities of, emotional states of, and relationships between people in an image. We draw conclusions based not just on what we see, but also from a lifetime of experience of living and interacting with other people. In this dissertation, we propose contextual features and models for understanding images of people with the objective of providing computers with access to the same contextual information that humans use. 
We show through a series of visual experiments that humans can exploit contextual knowledge to understand images of people. Recognizing other people becomes easier when the full body is shown instead of just the face. Social context is exploited to assign faces to corresponding first names, and age and gender recognition is improved when subjects see a face from an image in context with the other faces from the image instead of only a single face. 
In this dissertation, we provide contextual features and probabilistic frameworks to allow the computer to interpret images of people with contextual information. We propose features related to clothing, groups of associated people, relative positions of people, first name popularity, anthropometric measurements, and social relationships. The contextual features are learned from image data and from publicly available data from research organizations. By considering the context, we show improvement in a number of understanding tasks related to images of people. When applied to collections of multiple people, we show that context improves the identification of others in the collection. When considering single images, we show that context allows us to improve estimates of demographic descriptions of age and gender, as well as allowing us to determine the most likely owner of a first name such as "Taylor". Finally, we show that context allows us to perform high-level tasks such as segmenting rows of people and identifying the horizon in a single image of a group of people. 
This work shows that people act in predictable ways, for example that human patterns of association contain regular structure that can be effectively modeled and learned. From a broad perspective, this work shows that by exploiting information that is learned about people (in any field of science) we can improve our understanding of images of people.

A framework for using context to understand images of people

This paper presents an algorithm for considering both stereo cues and structural priors to obtain a geometrically representative depth map from a narrow baseline stereo pair. We use stereo pairs captured with a consumer stereo camera and observe that traditional depth estimation using stereo matching techniques encounters difficulties related to the narrow baseline relative to the depth of the scene. However, monocular geometric cues based on attributes such as lines and the horizon provide additional hints about the global structure that stereo matching misses. We merge both monocular and stereo matching features in a piecewise planar reconstruction framework that is initialized with a discrete inference step, and refined with a continuous optimization to encourage the intersections of hypothesized planes to coincide with observed image lines. We show through our results on stereo pairs of manmade structures captured outside of the lab that our algorithm exploits the advantages of both approaches to infer a better depth map of the scene.

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Andrew C. Gallagher

Papers

Sparsely sampled imaging device having color and luminance photosite

Method for controlling tone scale of digital image

A method of and system for automatically determining a level of light falloff in an image

A framework for using context to understand images of people

Combining monocular geometric cues with traditional stereo cues for consumer camera stereo