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

Geotagging has become popular for many multimedia applications. In this chapter, we present an integrated and intuitive system for location-driven tag suggestion, in the form of tag-clouds, for geotagged photos. Potential tags from multiple sources are extracted and weighted. Sources include points of interest (POI) tags from a public Geographic Names Information System (GNIS) database, community tags from Flickr® pictures, and personal tags shared through users’ own, family, and friends’ photo collections. To increase the effectiveness of GNIS POI tags, bags of place-name tags are first retrieved, clustered, and then re-ranked using a combined tf-idf and spatial distance criteria. The community tags from photos taken in the vicinity of the input geotagged photo are ranked according to distance and visual similarity to the input photo. Personal tags from other personally related photos inherently carry a significant weight due more to their high relevance than to both the generic place-name tags and community tags, and are ranked by weights that decay over time and distance differences. Finally, a rich set of the most relevant location-driven tags is presented to the user in the form of individual tag clouds under the three mentioned source categories. The tag clouds act as intuitive suggestions for tagging an input image. We also discuss quantitative and qualitative findings from a user study that we conducted. Evaluation has revealed the respective benefits of the three categories toward the effectiveness of the integrated tag suggestion system.

Using Geotags to Derive Rich Tag-Clouds for Image Annotation

A method for compensation at least one digital image for light falloff. The digital image may be generated for example from a conventional image frame on a film roll. A plurality of pixel values is provided for each of at least one digital image. A light falloff compensation function is applied and according to that a light falloff correction parameter is determined. Both the light falloff compensation function and the light falloff correction parameter are used to generate an individual compensation value for each pixel element. The compensation value is applied to each pixel element. The method is also able to differentiate and correct the images according to light falloff generated by pure lens falloff or a light falloff generated by the lens and the flash falloff.

Method for compensating digital images for light falloff and an apparatus therefor

One important problem in computer vision is to provide a demographic description a person from an image. In practice, many of the state-of-the-art methods use only an analysis of the face to estimate the age and gender of a person of interest. We present a model that combines two problems, height estimation and demographic classification, which allows each to serve as context for the other. Our idea is to use a calibrated camera for measuring the height of people in the scene. Height is measured by jointly inferring across anthropometric dimensions, age, and gender using publicly available statistics. The height estimate provides context for recognizing the age and gender of the subject, and likewise age and gender conditions the distribution of the anthropometric features for estimating height. The performance of our method is explored on a new database of 127 people captured with a calibrated camera with recorded height, age, and gender. We show that estimating height leads to improvements in age and gender classification, and vice versa. To the best of our knowledge, our model produces the most accurate automatic height estimates reported, with the error having a standard deviation of 26.7 mm.

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Jointly estimating demographics and height with a calibrated camera

A method of sharpening a digital image having image pixels according to its material content, includes the steps of: generating a subject matter belief map corresponding spatially to the image pixels, having belief values indicating the likelihood that respective image pixels are representative of a particular subject matter, generating a noisy pixel belief map corresponding spatially to the image pixels having belief values indicating the likelihood that the modulation about respective pixels are due to system noise; generating gain map from the subject matter belief map and the noisy pixel belief map having values that indicate the degree of sharpening to be applied to the image pixels; and using the gain map to sharpen the image.

Method for sharpening a digital image without amplifying noise

A method of detecting an object of interest having a known size in a digital image, includes providing a range information including two or more range values indicating the distance of objects in the scene from a known reference frame; detecting a candidate object of interest in the image; determining range values corresponding to the candidate object of interest and using these range values and the known size of the object of interest to classify the candidate object of interest.

Andrew C. Gallagher

Papers

Using Geotags to Derive Rich Tag-Clouds for Image Annotation

Method for compensating digital images for light falloff and an apparatus therefor

Jointly estimating demographics and height with a calibrated camera

Method for sharpening a digital image without amplifying noise

Detecting objects of interest in digital images